Libro de Códigos FONDECYT 1170239
13 abril, 2020
A continuación podrá encontrar información resumida sobre las variables relativas al proyecto FONDECYT_1170239. Los datos son agregados y no permiten inferir ni individualizar casos. Las variables se encuentran estandarizadas en sus nombres para generar compatibilidad entre distintos software estadísticos. Al final de este documento encontraráun resumen con las variables y datos descriptivos generales totales.
library(readr)
BD_2020_input2 <-haven::read_dta("BD_2020.dta", encoding = "latin1")
BD_2020_input <- readxl::read_excel("G:/Mi unidad/DOCUMENTOS/6.TESIS 2018/Codebook_FONDECYT_1170239/a59ce30bf3a72a8a6df4a71290f8c646e47aa994/BD_2020_input.xlsx")
#BD_2020_input <- read_csv("BD_2020_input.csv")
#BD_2020_input <- readr::read_csv("BD_2020.csv")
#estandarizamos los nombres de columnas
#copiar_nombres(data.frame(original = colnames(BD_2020), clean_names = colnames(janitor::clean_names(BD_2020))))
library(magrittr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unioncols <- c('n_persona','a2','a3','a4','a5','a6','a7','a8','a9','a10','a11','a12_o1','a12_o2','a12_o3','a12_o4',
'a12_o5','a12_o6','a12_otra_1','a12_otra_2','a13','a14','a15','a16','a17','a18_top_1','a18_top_2',
'a18_top_3','a18_top_4','a18_top_5','a18_top_6','a18_top_7','a18_top_8','a18_top_9','a18_top_10',
'a19_top_1','a19_top_2','a19_top_3','a19_top_4','a20_1_top_1','a20_1_top_2','a20_1_top_3','a20_1_top_4',
'a20_1_top_5','a20_1_top_6','a20_1_top_7','a20_1_top_8','a20_1_top_9','a20_1_top_10','a20_1_top_11',
'a20_1_top_12','a20_2_top_13','a20_2_top_14','a20_2_top_15','a20_2_top_16','a20_2_top_17','a20_2_top_18',
'a20_2_top_19','a20_2_top_20','a20_2_top_21','a20_2_top_22','a20_2_top_23','a20_2_top_24','a21','a21_1',
'a23','a25','a27','a28','a29_01','a29_02','a29_1','a22','a24','a26','a30','a31_top_1','a31_top_2',
'a31_top_3','a31_top_4','a31_top_5','a31_top_6','a31_top_7','a31_top_8','a31_top_9','a31_top_10',
'a31_top_11','a31_top_12','a31_top_13','a31_top_14','a31_top_15','a31_top_16','a32','a33_coded_1',
'a33_coded_2','a33_coded_3','a34_top_1','a34_top_2','a34_top_3','a34_top_4','a34_top_5','a34_top_6',
'a34_top_7','a34_top_8','a34_top_9','a34_top_10','a35_top_1','a35_top_2','a35_top_3','a35_top_4',
'a35_top_5','a35_top_6','a36','a37_1','a37_2','a37_3','a38_top_1','a38_top_2','a38_top_3','a38_top_4',
'a38_top_5','a38_top_6','a38_top_7','a38_top_8','a38_top_9','a38_top_10','a38_top_11','a38_top_12',
'a38_top_13','a38_top_14','a39_top_1','a39_top_2','a39_top_3','a39_top_4','a39_top_5','a39_top_6',
'a39_top_7','a40','a41','a42','a43_top_1','a43_top_2','a43_top_3','a43_top_4','a43_top_5','a44','a45_o1',
'a45_o2','a45_o3','a45_o4','a45_o5','a46_top_1','a46_top_12','a46_top_13','a47_top_1','a47_top_2',
'a47_top_3','a47_top_4','a47_top_5','a47_top_6','a47_top_7','a48','a49','a50','a51_1','a51_2','a51_3',
'a52','a54','a56','a55','a57_1','a63','a58','a59','a60','a61','a62','zona','gse','edux','ocux','edad',
'tamano_de_la_empresa','a1x1_coded_1','a1x2_coded_1','a11_recod','a48a49','depre','estrechez','mujer',
'psicotrop','frec_acos21_1','frec_acos_ecotest','testigo','k2_muyelevado','k2_elevado','prec1','prec2',
'prec4','prec5','prec6','antiguedad','politrab','desempleo','trabdom','atpub','latdec_dic','prec8',
'prec9','estabilidad','prec7','democ_dic','vul1','vul2','vul3','vul4','vul5','vul6','vul7','vul',
'auditc','prec3r','aposoc1','aposoc2','aposoc3','aposoc4','aposoc5','aposoc6','aposoc_tot','tenpsi',
'iso','salgen','climasex','culorg1','culorg2','culorg3','cultura','lid_des1','lid_des2','lid_des3',
'lid_des4','lid_des5','lid_des6','lid_des7','lid_des8','lid_des9','lid_des10','lid_des11','lid_des12',
'lid_des13','lid_des14','lid_des1_rec','lid_des2_rec','lid_des3_rec','lid_des4_rec','lid_des5_rec','lid_des6_rec','lid_des7_rec','lid_des8_rec',
'lid_des9_rec','lid_des10_rec','lid_des11_rec','lid_des12_rec','lid_des13_rec','lid_des14_rec','naq1','naq2','naq3','naq4','naq5','naq6','naq7',
'naq8','naq9','naq10','naq11','naq12','naq13','naq14','naq15','naq16','naq17','naq18','naq19','naq20','naq21','naq22',
'naq1_rec','naq2_rec','naq3_rec','naq4_rec','naq5_rec','naq6_rec','naq7_rec','naq8_rec','naq9_rec','naq10_rec',
'naq11_rec','naq12_rec','naq13_rec','naq14_rec','naq15_rec','naq16_rec','naq17_rec','naq18_rec','naq19_rec','naq20_rec',
'naq21_rec','naq22_rec','naq_leyman','naq_my_e','naq_a','mas_horas','menos45','quieren_mas','prec3','prec','precr',
'precr1','tamano','r1','r2','r3','r4','r5','r6','r7','e1','e2','e3','la_rec','lf_rec','lc_rec','lf_leyman',
'la_leyman','lc_leyman','k6','s1','s2','s3','s4','s5','ocuprev','naq_r_af_rec','naq_r_ap_rec','naq_r_rt_rec',
'naq_af_leyman','naq_ap_leyman','naq_rt_leyman','naq_af_my_e','naq_ap_my_e','naq_rt_my_e','naq_af_a','naq_ap_a',
'naq_rt_a','satlab_rec','araucaria_acoso','acoso_auto','acoso_auto_rec','naq_sum_roc','naq_sum_roc_3cat','hombre','autonaq','naq23',
'naq24','naq23_rec','naq24_rec','naq_r_incivismo_rec','naq_inciv_leyman','prev3','superv','ocuprev_rec','sect_priv','cont_trab','org_sind','gse_ac3','jef_hogar','agr_fisica','acos_sex','lf_rec_logit','la_rec_logit',
'lc_rec_logit','naq_dic_logit','lid_des_sum_cuartil','naq_sum_cuartil','expclass_culorg_1','expclass_culorg_2','expclass_culorg_3','expclass_culorg_4','expclass_culorg_5','expclass_culorg_6',
'cluster_rec2','expclass_culorg_alt_1','expclass_culorg_alt_2','expclass_culorg_alt_3','expclass_culorg_alt_4','expclass_culorg_alt_5','expclass_culorg_alt_6','rec_expclass_culorg_alt_1','rec_expclass_culorg_alt_2','rec_expclass_culorg_alt_3','rec_expclass_culorg_alt_4',
'rec_expclass_culorg_alt_5','rec_expclass_culorg_alt_6','rec_expclass_culorg_1','rec_expclass_culorg_2','rec_expclass_culorg_3','rec_expclass_culorg_4','rec_expclass_culorg_5','rec_expclass_culorg_6','lid_des_dic',
'lid_des_dic_logit','bajarecompensa','altoesfuerzo','desbalance_dic','culorg1_ext','culorg2_ext','culorg3_ext','proc_res','bienst_tarea','laxo_estr','lid_des_my_e','cluster_rec2_binary','cluster_rec2_alt',
'naq_sum_roc3','educacion277','ocuprev_xi_am277','ing_hog_xi_am277','vul_hm','lf_leyman_hm','la_leyman_hm','lc_leyman_hm','depre_hm',
'psicotrop_hm','k2_elevado_hm','climasex_hm','jerarquia_acoso','jerarquia_acoso2','lid_des_leyman','acos_psico_freq','carga_dom','educacion_media',
'cont_def','ocuprev_nocalif','santiago','valpo','concep','expclass_naq_alt_1','expclass_naq_alt_2','expclass_naq_alt_3',
'expclass_naq_alt_4','expclass_naq_alt_5','expclass_naq_alt_6','rec_expclass_naq_alt_1','rec_expclass_naq_alt_2','rec_expclass_naq_alt_3','rec_expclass_naq_alt_4','rec_expclass_naq_alt_5','rec_expclass_naq_alt_6','cluster_rec2_naq_alt','turnos')
cols2 <- c("sbj_num","cluster_rec","cluster_alt","cluster_rec_alt","cluster_naq_alt","cluster_naq_alt_rec")
BD_2020_input2<- janitor::clean_names(BD_2020_input2)
BD_2020_input<- BD_2020_input %>%
janitor::clean_names()
BD_2020_input[cols] <- lapply(BD_2020_input[cols], factor)
BD_2020_input[cols2] <- lapply(BD_2020_input[cols2], as.character)
#DTA
#BD_2020_input2
#EXCEL
#BD_2020_input
#estas columnas tenían decimales que el excel no procesaba, por eso lo reemplacé por los datos de stata
malos<-c("weight", "classpost_culorg_alt1", "classpost_culorg_alt2", "classpost_culorg_alt3", "classpost_culorg_alt4", "classpost_culorg_alt5", "classpost_culorg_alt6", "classpost_naq_def_1", "classpost_naq_def_2", "classpost_naq_def_3", "classpost_naq_def_4", "classpost_naq_def_5", "classpost_naq_def_6", "desbalance")
for (i in malos) {
BD_2020_input[i]<-BD_2020_input2[i]
}
#_#_#_#_#_#_#_#_#_#_#_#_#
#procesed file
#BD_2020_input <- readr::read_csv("BD_2020.csv")
#_#_#_#_#_#_#_#_#_#_#_#_#
#Ahora defino el metaarchivo asociado
metadata(BD_2020_input)$name <- "FONDECYT_1170239 A Marzo 2020"
metadata(BD_2020_input)$description <- "Prevalencia, formas de manifestacion y factores facilitadores de la violencia laboral, y sus efectos en la salud mental en poblacion asalariada en Chile atendiendo desiqualdades sociales y de genero. Inv. Responsable Elisa Ansoleaga"
metadata(BD_2020_input)$creator <- "Andres Gonzalez Santa Cruz"
metadata(BD_2020_input)$datePublished <- "2020-03-24"
#revisar por la presencia de eso: ^^^
codebook::var_label(BD_2020_input) <- list(sbj_num= 'SbjNum',
n_persona= 'A. ¿Cuántas personas de 20 o más años que trabajan en forma dependiente o asalariada, viven actualmente en esta vivienda?',
a2= 'En la empresa u organización donde usted trabaja ¿Supervisa a empleados/as?',
a3= '¿A qué actividad económica se dedica la org. en la que trabaja?',
a4= '¿Usted trabaja para un organismo público o para una empresa privada?',
a5= 'En este trabajo ¿Tiene contrato o acuerdo de trabajo escrito?',
a6= '¿Qué tipo de contrato o acuerdo de trabajo tiene?',
a7= 'Incluyéndose Ud ¿cuántas personas trabajan en su lugar de trabajo?',
a8= 'Habitualmente en su trabajo principal, ¿cuántas hrs trabaja en la semana?',
a9= '¿Usted desearía trabajar?',
a10= 'Respecto a su horario de trabajo, ¿este es?',
a11= 'Podría decir si la remuneración de su trabajo principal, es:',
a12_o1= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_o2= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_o3= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_o4= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_o5= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_o6= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_otra_1= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a12_otra_2= 'Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?',
a13= '¿Puede decirnos cuánto tiempo lleva trabajando en esta empresa u organización?',
a14= 'Además del trabajo principal, ¿usted destina tiempo a otro trabajo pagado?',
a14_1= '¿Cuántas horas le dedica a la semana en total a este otro trabajo pagado?',
a15= 'En los últimos 12 meses ¿usted ha estado desempleado?',
a15_1= 'En los últimos 12 meses, ¿Por cuánto tiempo ha estado desempleado?',
a16= 'Sin considerar trabajo remunerado (semana), ¿dedica tiempo a tareas domésticas no remuneradas?',
a16_1= 'Y aproximadamente, ¿Cuántas horas a la semana le dedica al trabajo doméstico y/o de cuidado en total?',
a17= 'En su trabajo, ¿tiene habitualmente contacto directo con público, de viva voz o por teléfono (pacientes, clientes, alumnos, apoderados)?',
a18_top_1= 'Mi trabajo exige que yo aprenda cosas nuevas (JCQ)',
a18_top_2= 'Mi trabajo exige un alto nivel de calificaciones o habilidades (JCQ)',
a18_top_3= 'Mi trabajo consiste en hacer siempre las mismas cosas (JCQ)',
a18_top_4= 'Tengo libertad de decidir cómo hacer mi trabajo (JCQ)',
a18_top_5= 'Tengo suficiente influencia en cómo ocurren las cosas en mi trabajo (JCQ)',
a18_top_6= 'Mi trabajo exige trabajar muy rápido (JCQ)',
a18_top_7= 'Me piden hacer una cantidad excesiva de trabajo (JCQ)',
a18_top_8= 'Tengo suficiente tiempo para hacer mi trabajo (JCQ)',
a18_top_9= 'Recibo demandas contradictorias por parte de otras personas (JCQ)',
a18_top_10= 'Mi trabajo me exige un trabajo mental muy intenso (JCQ)',
a19_top_1= '¿Hay en su trabajo momentos y/o situaciones que le producen desgaste emocional? (DEMOC)',
a19_top_2= 'En general, ¿considera usted que su trabajo le produce desgaste emocional? (DEMOC)',
a19_top_3= 'En su trabajo, ¿tiene usted que guardar sus emociones y no expresarlas? (DEMOC)',
a19_top_4= 'En su trabajo, ¿tiene usted que guardar sus opiniones y no expresarlas? (DEMOC)',
a20_1_top_1= 'Alguien le ha ocultado información que ha afectado a su rendimiento.(NAQ1)',
a20_1_top_2= 'Ha sido humillado o ridiculizado en relación a su trabajo.(NAQ2)',
a20_1_top_3= 'Le han ordenado realizar un trabajo que está por debajo de su nivel de competencia o calificación.(NAQ3)',
a20_1_top_4= 'Le han cambiado de realizar tareas de responsabilidad por otras más triviales o desagradables.(NAQ4)',
a20_1_top_5= 'Se han extendido rumores sobre usted.(NAQ5)',
a20_1_top_6= 'Ha sido ignorado, excluido o le han dejado de hablar.(NAQ6)',
a20_1_top_7= 'Le han insultado u ofendido con comentarios sobre usted, sus actitudes o su vida privada.(NAQ7)',
a20_1_top_8= 'Le han gritado o ha sido objeto de enfados espontáneos.(NAQ8)',
a20_1_top_9= 'Ha sufrido conductas intimidatorias como ser apuntado con el dedo, la invasión de su espacio personal, empujones, que no le dejen pasar, etc.(NAQ9)',
a20_1_top_10= 'Ha visto detalles o indirectas de otros que le sugieran abandonar su trabajo.(NAQ10)',
a20_1_top_11= 'Le han recordado continuamente sus errores y fallos.(NAQ11)',
a20_1_top_12= 'Ha sido ignorado o ha recibido una reacción hostil cuando se ha acercado a alguien.(NAQ12)',
a20_2_top_13= 'Ha recibido críticas persistentes sobre su trabajo y esfuerzo(NAQ13)',
a20_2_top_14= 'Sus opiniones y puntos de vista han sido ignorados.(NAQ14)',
a20_2_top_15= 'Ha recibido bromas pesadas de gente con la que no se lleva bien.(NAQ15)',
a20_2_top_16= 'Le han asignado tareas u objetivos inalcanzables.(NAQ16)',
a20_2_top_17= 'Ha recibido acusaciones (reclamos) en su contra.(NAQ17)',
a20_2_top_18= 'Ha sido excesivamente supervisado en su trabajo.(NAQ18)',
a20_2_top_19= 'Ha sido presionado para no reclamar algo a lo que tiene derecho (p. Ej., licencia por enfermedad, vacaciones, viáticos, etc.)(NAQ19)',
a20_2_top_20= 'Ha sido objeto de numerosas tomaduras de pelo y sarcasmos.(NAQ20)',
a20_2_top_21= 'Ha sido expuesto a una excesiva carga de trabajo.(NAQ21)',
a20_2_top_22= 'Ha recibido amenazas de violencia o de abusos físicos.(NAQ22)',
a20_2_top_23= 'Han dudado de su juicio sobre un asunto en el que tiene responsabilidad.',
a20_2_top_24= 'Ha sido ignorado o excluido por el resto de sus compañeros o grupo profesional.',
a21= 'Si definimos el acoso como una situación en la que una persona percibe que continuamente es objeto de conductas negativas frente a las cuales tiene dificultades para defenderse ¿según esta definición, usted ha sido acosado/a durante los últimos 6 mese',
a21_1= 'Y, ¿qué tan frecuentemente ha ocurrido ese acoso?',
a23= '¿Con qué frecuencia le ha ocurrido esto?',
a25= '¿Con qué frecuencia le ha ocurrido esto?',
a27= '¿Con qué frecuencia le ha ocurrido esto?',
a28= '¿En los últimos 12 meses, en su trabajo principal ¿usted ha sido testigo de conductas, palabras o gestos de carácter sexual no deseado?',
a29_01= 'Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores?',
a29_02= 'Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores?',
a29_1= '¿De qué sexo han sido los agresores?',
a22= 'Ahora, si consideramos los últimos 12 meses, en su trabajo principal, ¿ha sido alguna vez objeto de acoso psicológico, es decir de palabras o actos repetidos que atacan su dignidad o integridad?',
a24= '¿En su trabajo principal, en los últimos 12 meses, ha sido víctima de agresión física?',
a26= 'En los últimos 12 meses, en su trabajo principal ¿ha sido objeto de palabras o gestos de carácter sexual no deseado?',
a30= 'En caso de que usted haya sido objeto de alguna de las conductas señaladas, como acoso psicológico, agresión física o palabras o gestos de carácter sexual, ¿ha hecho algo para enfrentarlas?',
a31_top_1= 'Evitar encontrarse con los/as agresores/as',
a31_top_2= 'Hacer uso de licencias médicas',
a31_top_3= 'Evitar pensar en los hechos',
a31_top_4= 'Volcarse en otras actividades (deporte, salidas, recreación en general)',
a31_top_5= 'Renunciar a su trabajo',
a31_top_6= 'Buscar apoyo en las creencias religiosas o espirituales.',
a31_top_7= 'Realizar planes para mejorar la situación y llevarlos a cabo.',
a31_top_8= 'Buscar información para solucionar la situación.',
a31_top_9= 'Comentar lo sucedido con sus compañeros/as de trabajo.',
a31_top_10= 'Comentar lo sucedido con familiares y/o amigos/as',
a31_top_11= 'Buscar ayuda en personas que sufren el mismo problema.',
a31_top_12= 'Hablar con el/los involucrado/s',
a31_top_13= 'Hablar con superiores.',
a31_top_14= 'Denunciar los hechos al sindicato',
a31_top_15= 'Tomar acciones legales',
a31_top_16= 'Otras acciones',
a32= 'Y ¿usted ha vivido alguna situación en la que hayan sido vulnerados o pasados a llevar sus derechos en el trabajo?',
a33_coded_1= '¿Qué situación?, descríbala por favor.',
a33_coded_2= '¿Qué situación?, descríbala por favor.',
a33_coded_3= '¿Qué situación?, descríbala por favor.',
a34_top_1= 'A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado (e1)',
a34_top_2= 'Me interrumpen o molestan con frecuencia en mi trabajo (e2)',
a34_top_3= 'En el último tiempo tengo cada vez más trabajo (e3)',
a34_top_4= 'Mis superiores me dan el reconocimiento que merezco (r1)',
a34_top_5= 'Las oportunidades de promoción en mi trabajo son escasas (r2)',
a34_top_6= 'Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.) (r3)',
a34_top_7= 'Mi estabilidad laboral es baja (r4)',
a34_top_8= 'Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado (r5)',
a34_top_9= 'Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas (r6)',
a34_top_10= 'Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado (r7)',
a35_top_1= 'Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista)',
a35_top_2= 'Mi jefe inmediato consigue que la gente trabaje junta',
a35_top_3= 'Mi jefe inmediato facilita la realización del trabajo',
a35_top_4= 'Los(as) colegas con quienes trabajo se interesan o preocupan por mí',
a35_top_5= 'Mis colegas facilitan la realización de mi trabajo',
a35_top_6= 'En mi trabajo tengo la impresión de formar parte de un equipo',
a36= '¿En su lugar de trabajo hay un clima sexista?',
a37_1= 'En mi empresa, (institución, organización) lo importante es cómo se hacen las cosas, no importan tanto los resultados.^^^En mi empresa, (institución, organización) lo importante son los resultados, no importa tanto cómo se hacen las cosas.',
a37_2= 'En mi empresa, (institución, organización) se preocupan del bienestar de los trabajadores más que del cumplimiento de las tareas.^^^En mi empresa, (institución, organización) se preocupan del cumplimiento de las tareas más que del bienestar de los trabajad',
a37_3= 'En mi empresa, (institución, organización) hay poca planificación y control, a veces hay que improvisar^^^En mi empresa, (institución, organización) hay una disciplina de trabajo muy estricta, todo está controlado y resulta predecible',
a38_top_1= '¿Lo ha humillado a usted o a otros empleados/as, si no alcanzan los estándares esperados?(LID_DES1)',
a38_top_2= '¿Ha incentivado las ideas o propuestas creativas o innovadoras?(LID_DES2)',
a38_top_3= '¿Ha mostrado preocupación por los resultados?(LID_DES3)',
a38_top_4= '¿Ha hecho gestos (por ejemplo, muecas, miradas, ademanes) a usted o a otros empleados para demostrar que no está satisfecho con su esfuerzo o el de los demás?(LID_DES4)',
a38_top_5= '¿Ha evitado tomar decisiones?(LID_DES5)',
a38_top_6= 'El supervisor/la supervisora ¿reconoce el buen desempeño?(LID_DES6)',
a38_top_7= '¿Ha evitado involucrarse en su trabajo?(LID_DES7)',
a38_top_8= '¿Ha difundido información incorrecta sobre usted o sus compañeros/as de trabajo, con el fin de dañar su posición o la de ellos en la empresa?(LID_DES8)',
a38_top_9= '¿Ha estado ausente cuando se le/a necesita?(LID_DES9)',
a38_top_10= '¿Le ha retado por teléfono, le colgó (cortó), o enviado un correo grosero porque pensó que había hecho un mal trabajo?(LID_DES10)',
a38_top_11= '¿Busca la manera de desarrollar sus capacidades?(LID_DES11)',
a38_top_12= '¿Establece objetivos claros e inequívocos?(LID_DES12)',
a38_top_13= '¿Define y explica claramente las tareas que le asigna a usted y a sus compañeros de trabajo?(LID_DES13)',
a38_top_14= '¿Es flexible y está dispuesto/a a pensar de manera innovadora?(LID_DES14)',
a39_top_1= 'Tiene miedo de reclamar mejores condiciones de trabajo',
a39_top_2= 'Se siente indefenso/a ante el trato injusto de sus superiores',
a39_top_3= 'Se siente preocupado de que le cambien las condiciones de su salario',
a39_top_4= 'Tiene miedo de que lo despidan si no hace lo que le piden',
a39_top_5= 'Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato',
a39_top_6= 'Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan',
a39_top_7= 'Le hacen sentir que usted puede ser fácilmente reemplazado (a)',
a40= '¿En su empresa o institución existe alguna organización sindical o gremial?',
a41= 'Pertenece usted a alguna organización sindical o gremial',
a42= '¿Tiene usted algún cargo de representación sindical?',
a43_top_1= 'Me siento bastante satisfecho con mi trabajo actual (s1)',
a43_top_2= 'Casi todos los días estoy entusiasmado con mi trabajo (s2)',
a43_top_3= 'Cada día en el trabajo parece que nunca terminará (s3)',
a43_top_4= 'Disfruto con mi trabajo (s4)',
a43_top_5= 'Considero que mi trabajo es bastante desagradable (s5)',
a44= 'En general, diría usted que su salud es…',
a45_o1= 'En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?',
a45_o2= 'En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?',
a45_o3= 'En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?',
a45_o4= 'En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?',
a45_o5= 'En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?',
a46_top_1= '¿Disminuir la ansiedad o el nerviosismo, tales como ansiolíticos? (CON_PSI)',
a46_top_12= '¿Ayudar a dormir, tales como hipnóticos? (CON_PSI)',
a46_top_13= '¿Remontar el ánimo tales como antidepresivos? (CON_PSI)',
a47_top_1= 'Nervioso/a (K6)',
a47_top_2= 'Desesperado/a (K6)',
a47_top_3= 'Descontrolado/a o fuera de sí (K6)',
a47_top_4= 'Tan deprimido/a que nada le puede hacer sonreír (K6)',
a47_top_5= 'Que todo es un esfuerzo (K6)',
a47_top_6= 'Bueno/a para nada (K6)',
a47_top_7= 'Sin esperanza (K6)',
a48= 'Durante los últimos 12 meses, ¿se sintió triste o deprimido/a por dos semanas consecutivas o más?',
a49= 'Durante los últimos 12 meses, ¿llegó a sentir la pérdida de interés por la mayoría de las cosas que le gustaba hacer, tales como su trabajo, pasatiempos o cualquier actividad, por dos semanas seguidas o más?',
a50= 'Si usted ha sentido alguno de los problemas de salud antes señalados, como sentirse nervioso/a o con miedo, triste, deprimido/a, o ha consumido algún medicamento ¿a qué lo/s atribuye?',
a51_1= 'A continuación, quisiera hacerle algunas preguntas respecto a consumo de alcohol. ¿Qué tan seguido toma usted alguna bebida alcohólica?',
a51_2= '¿Cuántos tragos suele tomar usted en un día típico de consumo de alcohol?',
a51_3= '¿Qué tan seguido toma usted 5 o más tragos en una sola ocasión?',
a52= 'Sexo',
a53= 'Edad (años cumplidos)',
a54= 'Indíqueme por favor, ¿cuál es su nivel educacional?',
a56= 'Por favor, indíqueme el nivel educacional del jefe de hogar',
a55= '¿Es usted el/la jefe/a de hogar?',
a57_1= 'Jefe de Hogar:',
a63= 'TRABAJO DEL ENTREVISTADO:',
a58= '¿Nos podría decir en cuál de los siguientes tramos se encuentra su ingreso personal mensual? (considerando todos sus ingresos)',
a59= 'Situación de pareja actual',
a60= '¿Cuántas personas dependen económicamente de usted (totalmente o en parte)?',
a61= 'Por favor, indique la frase que más se acerca a la realidad de su hogar: “Pensando en el total de ingresos mensuales de su hogar, este…',
a62= '¿Me podría decir ¿en cuál de los siguientes tramos se encuentra aproximadamente el ingreso mensual total de su hogar?',
weight= 'Weight (ponderador)',
zona= 'Región (Adimark)',
gse= 'GSE Encuestado/a (Adimark)',
edux= 'Escolaridad (Adimark)',
ocux= 'Situación Ocupacional (Adimark)',
edad= 'Edad (en grupos) (Adimark)',
tamano_de_la_empresa= 'Tamaño de la Empresa (Adimark)',
a1x1_coded_1= 'Ocupación (Adimark)',
a1x2_coded_1= 'Cargo (Adimark)',
a11_recod= ' Podría decir si la remuneración de su trabajo principal, es: (Recodificado) (Adimark)',
a48a49= 'Personas con síntomas depresivos (Adimark)',
depre= 'Sintomatología depresiva',
estrechez= 'Estrechez Económica',
mujer= 'Sexo (1=Mujer)',
psicotrop= 'Consumo de psicotrópicos',
frec_acos21_1= 'Autocategorización Acoso Frecuente (Varias veces por semana y A diario)',
frec_acos_ecotest= 'Presencia acoso psicologico Araucaria (A22)',
testigo= 'Testigo de acoso sexual (A28)',
k2_muyelevado= 'Distrés muy elevado (>12 en suma de puntajes brutos)',
k2_elevado= 'Distrés elevado o muy elevado (>6)',
k6_2= 'Suma de puntajes brutos Distrés K6',
prec1= 'Ausencia Contrato Escrito',
prec2= 'Contrato precario (Honorarios u otro)',
prec4= 'Trabaja menos de 44 hrs y desea trabajar más',
prec5= 'Horario nocturno o por turnos',
prec6= 'Remuneración variable',
antiguedad= 'Antigüedad (Mayor a 6 meses) (A13)',
politrab= 'Politrabajo (A14)',
desempleo= 'Desempleo últimos 12 meses (A15)',
trabdom= 'Trabajo Doméstico (A16)',
atpub= 'Atención a Público',
khab= 'JCQ desarrollo de habilidades',
kcont= 'JCQ control',
latdec= 'JCQ latitud decisional',
latdec_dic= 'Baja latitud decisional (Dicotomizada)',
democ= 'Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21)',
prec8= 'Desempleo últimos 12 meses',
prec9= 'Ingreso Personal Mensual (<350000 pesos)',
estabilidad= 'Baja estabilidad laboral',
prec7= 'Baja estabilidad laboral',
democ_dic= 'Altas demandas emocionales (Dicotomizada)',
vul1= 'Tiene miedo de reclamar mejores condiciones de trabajo',
vul2= 'Se siente indefenso/a ante el trato injusto de sus superiores',
vul3= 'Se siente preocupado de que le cambien las condiciones de su salario',
vul4= 'Tiene miedo de que lo despidan si no hace lo que le piden',
vul5= 'Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato',
vul6= 'Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan',
vul7= 'Le hacen sentir que usted puede ser fácilmente reemplazado (a)',
vul= 'Vulnerabilidad, suma de puntajes (1= 3 o más)',
auditc= 'Consumo riesgoso de Alcohol (ACC)',
prec3r= 'Trabaja menos de 44 hrs y desea trabajar más',
aposoc1= 'Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista) (recodificada sin NR)',
aposoc2= 'Mi jefe inmediato consigue que la gente trabaje junta (recodificada sin NR)',
aposoc3= 'Mi jefe inmediato facilita la realización del trabajo (recodificada sin NR)',
aposoc4= 'Los(as) colegas con quienes trabajo se interesan o preocupan por mí (recodificada sin NR)',
aposoc5= 'Mis colegas facilitan la realización de mi trabajo (recodificada sin NR)',
aposoc6= 'En mi trabajo tengo la impresión de formar parte de un equipo (recodificada sin NR)',
aposoc_tot= 'Bajo apoyo social',
tenpsi= 'JOBSTRAIN/Tensión Psíquica (Dicotomizada)',
iso= 'ISOSTRAIN',
salgen= 'Salud General (1=Mala o Muy Mala)',
climasex= 'Clima Sexista',
culorg1= 'CO orientada a Resultados (vs. a Procesos) (Art JPT)',
culorg2= 'CO orientada a Cumplimiento de Tareas (vs. Bienestar de Trabajadores) (Art JPT)',
culorg3= 'CO orientada a Control Estricto (vs. Laxo) (Art JPT)',
cultura= 'C.O. Orientada a resultados, tareas y rígida',
lid_des1= 'Variable LID_DES1 recodificada (sin datos perdidos)',
lid_des2= 'Variable LID_DES2 recodificada (sin datos perdidos)',
lid_des3= 'Variable LID_DES3 recodificada (sin datos perdidos)',
lid_des4= 'Variable LID_DES4 recodificada (sin datos perdidos)',
lid_des5= 'Variable LID_DES5 recodificada (sin datos perdidos)',
lid_des6= 'Variable LID_DES6 recodificada (sin datos perdidos)',
lid_des7= 'Variable LID_DES7 recodificada (sin datos perdidos)',
lid_des8= 'Variable LID_DES8 recodificada (sin datos perdidos)',
lid_des9= 'Variable LID_DES9 recodificada (sin datos perdidos)',
lid_des10= 'Variable LID_DES10 recodificada (sin datos perdidos)',
lid_des11= 'Variable LID_DES11 recodificada (sin datos perdidos)',
lid_des12= 'Variable LID_DES12 recodificada (sin datos perdidos)',
lid_des13= 'Variable LID_DES13 recodificada (sin datos perdidos)',
lid_des14= 'Variable LID_DES14 recodificada (sin datos perdidos)',
lid_des1_rec= 'Variable LID_DES1 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des2_rec= 'Variable LID_DES2 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des3_rec= 'Variable LID_DES3 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des4_rec= 'Variable LID_DES4 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des5_rec= 'Variable LID_DES5 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des6_rec= 'Variable LID_DES6 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des7_rec= 'Variable LID_DES7 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des8_rec= 'Variable LID_DES8 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des9_rec= 'Variable LID_DES9 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des10_rec= 'Variable LID_DES10 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des11_rec= 'Variable LID_DES11 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des12_rec= 'Variable LID_DES12 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des13_rec= 'Variable LID_DES13 recodificada (sin datos perdidos en base a criterio Leymann)',
lid_des14_rec= 'Variable LID_DES14 recodificada (sin datos perdidos en base a criterio Leymann)',
lf= 'Suma de puntajes brutos Liderazgo Laissez-Faire (todas las variables)',
la= 'Suma de puntajes brutos Liderazgo Autoritario',
lc= 'Suma de puntajes brutos Liderazgo Constructivo (todas las variables)',
naq1= 'Variable NAQ1 recodificada (sin datos perdidos)',
naq2= 'Variable NAQ2 recodificada (sin datos perdidos)',
naq3= 'Variable NAQ3 recodificada (sin datos perdidos)',
naq4= 'Variable NAQ4 recodificada (sin datos perdidos)',
naq5= 'Variable NAQ5 recodificada (sin datos perdidos)',
naq6= 'Variable NAQ6 recodificada (sin datos perdidos)',
naq7= 'Variable NAQ7 recodificada (sin datos perdidos)',
naq8= 'Variable NAQ8 recodificada (sin datos perdidos)',
naq9= 'Variable NAQ9 recodificada (sin datos perdidos)',
naq10= 'Variable NAQ10 recodificada (sin datos perdidos)',
naq11= 'Variable NAQ11 recodificada (sin datos perdidos)',
naq12= 'Variable NAQ12 recodificada (sin datos perdidos)',
naq13= 'Variable NAQ13 recodificada (sin datos perdidos)',
naq14= 'Variable NAQ14 recodificada (sin datos perdidos)',
naq15= 'Variable NAQ15 recodificada (sin datos perdidos)',
naq16= 'Variable NAQ16 recodificada (sin datos perdidos)',
naq17= 'Variable NAQ17 recodificada (sin datos perdidos)',
naq18= 'Variable NAQ18 recodificada (sin datos perdidos)',
naq19= 'Variable NAQ19 recodificada (sin datos perdidos)',
naq20= 'Variable NAQ20 recodificada (sin datos perdidos)',
naq21= 'Variable NAQ21 recodificada (sin datos perdidos)',
naq22= 'Variable NAQ22 recodificada (sin datos perdidos)',
naq1_rec= 'Variable NAQ1 recodificada (sin datos perdidos en base a criterio Leymann)',
naq2_rec= 'Variable NAQ2 recodificada (sin datos perdidos en base a criterio Leymann)',
naq3_rec= 'Variable NAQ3 recodificada (sin datos perdidos en base a criterio Leymann)',
naq4_rec= 'Variable NAQ4 recodificada (sin datos perdidos en base a criterio Leymann)',
naq5_rec= 'Variable NAQ5 recodificada (sin datos perdidos en base a criterio Leymann)',
naq6_rec= 'Variable NAQ6 recodificada (sin datos perdidos en base a criterio Leymann)',
naq7_rec= 'Variable NAQ7 recodificada (sin datos perdidos en base a criterio Leymann)',
naq8_rec= 'Variable NAQ8 recodificada (sin datos perdidos en base a criterio Leymann)',
naq9_rec= 'Variable NAQ9 recodificada (sin datos perdidos en base a criterio Leymann)',
naq10_rec= 'Variable NAQ10 recodificada (sin datos perdidos en base a criterio Leymann)',
naq11_rec= 'Variable NAQ11 recodificada (sin datos perdidos en base a criterio Leymann)',
naq12_rec= 'Variable NAQ12 recodificada (sin datos perdidos en base a criterio Leymann)',
naq13_rec= 'Variable NAQ13 recodificada (sin datos perdidos en base a criterio Leymann)',
naq14_rec= 'Variable NAQ14 recodificada (sin datos perdidos en base a criterio Leymann)',
naq15_rec= 'Variable NAQ15 recodificada (sin datos perdidos en base a criterio Leymann)',
naq16_rec= 'Variable NAQ16 recodificada (sin datos perdidos en base a criterio Leymann)',
naq17_rec= 'Variable NAQ17 recodificada (sin datos perdidos en base a criterio Leymann)',
naq18_rec= 'Variable NAQ18 recodificada (sin datos perdidos en base a criterio Leymann)',
naq19_rec= 'Variable NAQ19 recodificada (sin datos perdidos en base a criterio Leymann)',
naq20_rec= 'Variable NAQ20 recodificada (sin datos perdidos en base a criterio Leymann)',
naq21_rec= 'Variable NAQ21 recodificada (sin datos perdidos en base a criterio Leymann)',
naq22_rec= 'Variable NAQ22 recodificada (sin datos perdidos en base a criterio Leymann)',
naq_dic= 'Suma puntajes dicotomizado (NAQ) (Dichotomized sum score)',
naq_leyman= 'NAQ Criterio Leymann',
naq_my_e= 'NAQ Criterio Mikkelsen y Einarsen',
naq_a= 'NAQ Criterio Agervold',
mas_horas= 'Desearía trabajar más horas a la semana (A9)',
menos45= 'Trabajo principal de menos 45 horas (A8)',
quieren_mas= 'Desearía trabajar más horas a la semana (A9)',
prec3= 'Trabaja menos de 45 horas semanales y desearia trabajar más',
prec= 'Indicador de precariedad',
precr= 'Indicador de precariedad',
precr1= '3 o más indicadores de precariedad laboral (de suma prec1 2 3 5 6 7 8 y vul)',
tamano= 'Tamaño de la organización (Adimark)',
r1= 'Mis superiores me dan el reconocimiento que merezco',
r2= 'Las oportunidades de promoción en mi trabajo son escasas',
r3= 'Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.)',
r4= 'Mi estabilidad laboral es baja',
r5= 'Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado',
r6= 'Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas',
r7= 'Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado',
e1= 'A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado',
e2= 'Me interrumpen o molestan con frecuencia en mi trabajo',
e3= 'En el último tiempo tengo cada vez más trabajo',
esfuerzo= 'Suma Esfuerzos',
recompensas= 'Suma Recompensas',
la_rec= 'Suma ptjes dicotomizados Leyman (1,4,8,10) Lid Autoritario',
lf_rec= 'Suma ptjes dicotomizados Leyman (5,7,9) Lid Laissez-Faire',
lc_rec= 'Suma ptjes dicotomizados Leyman (6,11,12,14) Lid Constructivo',
lf_leyman= 'Exposición a Lid. Laissez-Faire (Leymann)',
la_leyman= 'Exposición a Lid. Autoritario/Tiránico (Leymann)',
lc_leyman= 'Exposición a Lid. Constructivo (Leymann)',
naq_sum= 'Acosados de vez en cuando o víctimas de acoso (Puntaje Bruto NAQ)',
k6= 'Distrés elevado o muy elevado (>6)',
s1= 'Me siento bastante satisfecho con mi trabajo actual',
s2= 'Casi todos los días estoy entusiasmado con mi trabajo',
s3= 'Cada día en el trabajo parece que nunca terminará',
s4= 'Disfruto con mi trabajo',
s5= 'Considero que mi trabajo es bastante desagradable (invertido)',
satis= 'Suma de Puntajes Brutos Satisfacción Laboral',
ocuprev= 'Ocupación Araucaria (codificada erroneamente, ver ocuprev rec)',
naq_r_af_rec= 'Suma de puntajes dic (Al menos una vez a la semana) Acoso Físico',
naq_r_ap_rec= 'Suma de puntajes dic (Al menos una vez a la semana) Acoso Psicológico',
naq_r_rt_rec= 'Suma de puntajes dic (Al menos una vez a la semana) Rel con Trabajo',
naq_af_leyman= 'NAQ Acoso Físico (Leymann)',
naq_ap_leyman= 'NAQ Acoso Psicológico (Leymann)',
naq_rt_leyman= 'NAQ Relacionado con el Trabajo (Leymann)',
naq_af_my_e= 'NAQ Acoso Físico (Mikkelsen y Einarsen)',
naq_ap_my_e= 'NAQ Acoso Psicológico (Mikkelsen y Einarsen)',
naq_rt_my_e= 'NAQ Relacionado con el Trabajo (Mikkelsen y Einarsen)',
naq_af_a= 'NAQ Acoso Físico (Agervold)',
naq_ap_a= 'NAQ Acoso Psicológico (Agervold)',
naq_rt_a= 'NAQ Relacionado con el Trabajo (Agervold)',
satlab_rec= 'Baja Satisfacción Laboral (1er cuartil suma de puntajes)',
araucaria_acoso= 'Suma acoso sexual, psicológico y físico, Araucaria',
acoso_auto= 'Suma acoso sexual, psicológico y físico, Araucaria, más Autocategorización NAQ',
acoso_auto_rec= 'Al menos una forma de victimización acoso sexual, físico, psi o autonaq',
naq_sum_roc= 'Variable Dicotomizada Suma Puntajes Brutos NAQ Curvas ROC (>11 ptos) (tesis ags)',
naq_sum_roc_3cat= 'Diferencia entre quienes obtienen >11 ptos, >9 ptos, y 0 a 8 (tesis ags)',
hombre= 'Sexo Hombre (1=Hombre)',
autonaq= 'Autocategorización según Definición NAQ (A21)',
naq23= 'Variable NAQ23 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ',
naq24= 'Variable NAQ24 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ',
naq23_rec= 'Variable NAQ23 recodificada (sin datos perdidos en base a criterio Leymann)',
naq24_rec= 'Variable NAQ24 recodificada (sin datos perdidos en base a criterio Leymann)',
naq_r_incivismo_rec= 'Escala Incivismo NAQ (Suma items 7, 14, 23 y 24) (Am y Xi 2018)',
naq_inciv_leyman= 'Escala Incivismo NAQ (>0) (Am y Xi 2018)',
prev3= 'Ocupación Araucaria (Operarios, Técnicos y No Calificados v/s Oficinistas, Servicios, Jefaturas y Profesionales) (Art JPT)',
superv= 'Supervisa a otros',
ocuprev_rec= 'Ocupación Araucaria (Recodificada 5= Vendedores) (Art JPT)',
sect_priv= 'Trabaja Organismo Público o Empresa Privada (1=Sector privado)',
cont_trab= 'Ausencia de Contrato Escrito (1= Sin Contrato)',
org_sind= 'Existencia de Organización Sindical o Gremial (1=No)',
gse_ac3= 'GSE Encuestado (D y E) (1= DyE)',
jef_hogar= 'Jefe de Hogar (1=Sí)',
agr_fisica= 'Agresión física últimos 12 meses',
acos_sex= 'Acoso sexual últimos 12 meses',
lf_rec_logit= 'Suma puntajes dicotomizados Lid Laissez-Faire Leymann (OCM), 3 niveles(ags)',
la_rec_logit= 'Suma puntajes dicotomizados Lid Autoritario Leymann (OCM), 3 niveles(ags)',
lc_rec_logit= 'Suma puntajes dicotomizados Lid Constructivo Leymann (OCM), 3 niveles(ags)',
naq_dic_logit= 'Suma puntajes dicotomizados NAQ Leymann (OCM), 3 niveles(ags)',
lid_des_sum= 'Suma puntajes brutos (5, 7, 9, 1, 4, 8, 10) Lid Destructivo',
lid_des_sum_cuartil= 'NAQ por cuartiles (Por defecto, con 2000 casos)',
naq_sum_cuartil= 'Suma ptjes brutos NAQ-R (por cuartiles) (correcto 1995 casos)',
expclass_culorg_1= 'Pertenencia Esperada a la Clase 1 (>.5), Cultura por Clases (JPT)',
expclass_culorg_2= 'Pertenencia Esperada a la Clase 2 (>.5), Cultura por Clases (JPT)',
expclass_culorg_3= 'Pertenencia Esperada a la Clase 3 (>.5), Cultura por Clases (JPT)',
expclass_culorg_4= 'Pertenencia Esperada a la Clase 4 (>.5), Cultura por Clases (JPT)',
expclass_culorg_5= 'Pertenencia Esperada a la Clase 5 (>.5), Cultura por Clases (JPT)',
expclass_culorg_6= 'Pertenencia Esperada a la Clase 6 (>.5), Cultura por Clases (JPT)',
cluster_rec= 'Conjunto concatenado de clases (JPT)',
cluster_rec2= 'Recodificación Clases Latentes Cultura (1=.;2=6;3=5;4=4;5=3;6=2;7=1) (JPT)',
expclass_culorg_alt_1= 'Pertenencia Esperada a la Clase 1 (>.5), Cultura Alternativa por Clases (JPT)',
expclass_culorg_alt_2= 'Pertenencia Esperada a la Clase 2 (>.5), Cultura Alternativa por Clases (JPT)',
expclass_culorg_alt_3= 'Pertenencia Esperada a la Clase 3 (>.5), Cultura Alternativa por Clases (JPT)',
expclass_culorg_alt_4= 'Pertenencia Esperada a la Clase 4 (>.5), Cultura Alternativa por Clases (JPT)',
expclass_culorg_alt_5= 'Pertenencia Esperada a la Clase 5 (>.5), Cultura Alternativa por Clases (JPT)',
expclass_culorg_alt_6= 'Pertenencia Esperada a la Clase 6 (>.5), Cultura Alternativa por Clases (JPT)',
classprior1= 'Probabilidad A priori de Pertenecer a la Clase 1, CULTURA Definitiva por Clases (JPT)',
classprior2= 'Probabilidad A priori de Pertenecer a la Clase 2, CULTURA Definitiva por Clases (JPT)',
classprior3= 'Probabilidad A priori de Pertenecer a la Clase 3, CULTURA Definitiva por Clases (JPT)',
classprior4= 'Probabilidad A priori de Pertenecer a la Clase 4, CULTURA Definitiva por Clases (JPT)',
classprior5= 'Probabilidad A priori de Pertenecer a la Clase 5, CULTURA Definitiva por Clases (JPT)',
classprior6= 'Probabilidad A priori de Pertenecer a la Clase 6, CULTURA Definitiva por Clases (JPT)',
rec_expclass_culorg_alt_1= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 1 (6 clases) (JPT)',
rec_expclass_culorg_alt_2= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 2 (6 clases) (JPT)',
rec_expclass_culorg_alt_3= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 3 (6 clases) (JPT)',
rec_expclass_culorg_alt_4= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 4 (6 clases) (JPT)',
rec_expclass_culorg_alt_5= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 5 (6 clases) (JPT)',
rec_expclass_culorg_alt_6= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 6 (6 clases) (JPT)',
cluster_alt= 'Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT)',
cluster_rec_alt= 'Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT)',
rec_expclass_culorg_1= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 1 (6 clases) (JPT)',
rec_expclass_culorg_2= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 2 (6 clases) (JPT)',
rec_expclass_culorg_3= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 3 (6 clases) (JPT)',
rec_expclass_culorg_4= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 4 (6 clases) (JPT)',
rec_expclass_culorg_5= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 5 (6 clases) (JPT)',
rec_expclass_culorg_6= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 6 (6 clases) (JPT)',
naq_r_af_sum= 'Suma de puntajes brutos NAQ Acoso Físico (ags)',
naq_r_ap_sum= 'Suma de puntajes brutos NAQ Acoso Psicológico (ags)',
naq_r_rt_sum= 'Suma de puntajes brutos NAQ Acoso Relacionado con el Trabajo (ags)',
democ_paper= 'Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21) (ags)',
lf2= 'Suma de puntajes brutos Liderazgo Laissez-Faire (solo variables validadas) (ags)',
lc2= 'Suma de puntajes brutos Liderazgo Constructivo (solo variables validadas) (ags)',
lid_des_dic= 'Suma puntajes dicotomizado (DLS) (Dichotomized sum score) (ags)',
lid_des_dic_logit= 'Suma puntajes dicotomizados DLS Leymann (OCM), 3 niveles(ags)',
bajarecompensa= 'Baja Recompensa (Art JPT)',
altoesfuerzo= 'Alto Esfuerzo (Art JPT)',
desbalance= 'esfuerzo/(recompensas* 0.4285)',
desbalance_dic= 'Desbalance Esfuerzo-Recompensa',
culorg1_ext= 'CO Desbalanceada en Eje Resultados vs. Procesos (puntajes 3 y 4=0)',
culorg2_ext= 'CO Desbalanceada en Eje Control Estricto vs. Laxo (puntajes 3 y 4=0)',
culorg3_ext= 'CO Desbalanceada en Eje Tareas vs. Bienestar Trabajadores (puntajes 3 y 4=0)',
proc_res= 'CO Balanceada en Eje Resultados vs. Procesos (1=3/4) (JPT)',
bienst_tarea= 'CO Balanceada en Eje Tareas vs. Bienestar Trabajadores (1=3/4) (JPT)',
laxo_estr= 'CO Balanceada en Eje Control Estricto vs. Laxo (1=3/4) (JPT)',
lid_des_my_e= 'Exposición a Liderazgos Destructivos (OCM Mikkelsen y Einarsen) (Art JPT)',
cluster_rec2_binary= 'Clase a Comparar con el Resto de las Clases',
classpost_culorg_alt1= 'Probabilidad Posterior de Pertencer a la Clase 1, CULTURA Definitiva por Clases (JPT)',
classpost_culorg_alt2= 'Probabilidad Posterior de Pertencer a la Clase 2, CULTURA Definitiva por Clases (JPT)',
classpost_culorg_alt3= 'Probabilidad Posterior de Pertencer a la Clase 3, CULTURA Definitiva por Clases (JPT)',
classpost_culorg_alt4= 'Probabilidad Posterior de Pertencer a la Clase 4, CULTURA Definitiva por Clases (JPT)',
classpost_culorg_alt5= 'Probabilidad Posterior de Pertencer a la Clase 5, CULTURA Definitiva por Clases (JPT)',
classpost_culorg_alt6= 'Probabilidad Posterior de Pertencer a la Clase 6, CULTURA Definitiva por Clases (JPT)',
cluster_rec2_alt= 'Clasificación por Cluster, Ítemes de Cultura (6 clases)',
naq_sum_roc3= 'Suma ptjes brutos NAQ-R ROC (crit. inclusivo) (Art Xi y Am) (tesis ags)',
educacion277= 'Recodificación de Nivel Educacional (Art. Ximena y Amalia)',
ocuprev_xi_am277= 'Recodificación de Ocupación (Art. Ximena y Amalia)',
ing_hog_xi_am277= 'Recodificación de Ingreso Hogar Mensual (Art. Ximena y Amalia)',
vul_hm= 'Sólo con mujeres y hombres expuestos a vulnerabilidad (Art. Ximena y Amalia)',
lf_leyman_hm= 'Sólo con mujeres y hombres expuestos a Lid Laissez Faire (Leymann) (Art. Ximena y Amalia)',
la_leyman_hm= 'Sólo con mujeres y hombres expuestos a Lid Autoritario (Leymann) (Art. Ximena y Amalia)',
lc_leyman_hm= 'Sólo con mujeres y hombres expuestos a Lid Constructivo (Leymann) (Art. Ximena y Amalia)',
depre_hm= 'Sólo con mujeres y hombres expuestos a Sint Depresiva (Leymann) (Art. Ximena y Amalia)',
psicotrop_hm= 'Sólo con mujeres y hombres expuestos a Cons Psicotrópicos (Leymann) (Art. Ximena y Amalia)',
k2_elevado_hm= 'Sólo con mujeres y hombres expuestos a Distrés (Leymann) (Art. Ximena y Amalia)',
climasex_hm= 'Sólo con mujeres y hombres expuestos a Clima Sexista (Leymann) (Art. Ximena y Amalia)',
jerarquia_acoso= 'Expuestos a Acoso Psicológico, Físico o Sexual, Jerarquía Agresores (1)',
jerarquia_acoso2= 'Expuestos a Acoso Psicológico, Físico o Sexual, Jerarquía Agresores (2)',
lid_des_leyman= 'Exposición a Liderazgos Destructivos (OCM Leyman) (Art JPT)',
acos_psico_freq= 'Acoso Psicológico Seguido y Muy Seguido (Art. Ximena y Amalia)',
carga_dom= 'Y sin considerar su trabajo remunerado, durante la semana, ¿usted dedica además tiempo para hacer tareas domésticas y/o de cuidado, pero no remunerado? (A16)',
educacion_media= 'Educación Menor a Nivel de Enseñanza Media Completa (Art. Magdalena)',
cont_def= 'Contrato Definido (Art. Magdalena)',
ocuprev_nocalif= 'Ocupación Sin Calificación (Operarios, Técnicos, Conductores y Trabajdores No-Calificados) (Art. Magdalena)',
santiago= 'Zona, Gran Santiago',
valpo= 'Zona, Gran Valparaiso',
concep= 'Zona, Gran Concepción',
classpost_naq_def_1= 'Probabilidad Posterior de Pertencer a la Clase 1, NAQ por Clases (ags)',
classpost_naq_def_2= 'Probabilidad Posterior de Pertencer a la Clase 2, NAQ por Clases (ags)',
classpost_naq_def_3= 'Probabilidad Posterior de Pertencer a la Clase 3, NAQ por Clases (ags)',
classpost_naq_def_4= 'Probabilidad Posterior de Pertencer a la Clase 4, NAQ por Clases (ags)',
classpost_naq_def_5= 'Probabilidad Posterior de Pertencer a la Clase 5, NAQ por Clases (ags)',
classpost_naq_def_6= 'Probabilidad Posterior de Pertencer a la Clase 6, NAQ por Clases (ags)',
expclass_naq_alt_1= 'Pertenencia Esperada a la Clase 1 (>.6), NAQ por Clases (ags)',
expclass_naq_alt_2= 'Pertenencia Esperada a la Clase 2 (>.6), NAQ por Clases (ags)',
expclass_naq_alt_3= 'Pertenencia Esperada a la Clase 3 (>.6), NAQ por Clases (ags)',
expclass_naq_alt_4= 'Pertenencia Esperada a la Clase 4 (>.6), NAQ por Clases (ags)',
expclass_naq_alt_5= 'Pertenencia Esperada a la Clase 5 (>.6), NAQ por Clases (ags)',
expclass_naq_alt_6= 'Pertenencia Esperada a la Clase 6 (>.6), NAQ por Clases (ags)',
rec_expclass_naq_alt_1= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 1 (6 clases) (ags)',
rec_expclass_naq_alt_2= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 2 (6 clases) (ags)',
rec_expclass_naq_alt_3= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 3 (6 clases) (ags)',
rec_expclass_naq_alt_4= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 4 (6 clases) (ags)',
rec_expclass_naq_alt_5= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 5 (6 clases) (ags)',
rec_expclass_naq_alt_6= 'Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 6 (6 clases) (ags)',
cluster_naq_alt= 'Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) NAQ (ags)',
cluster_naq_alt_rec= 'Clasificación preliminar (contiene a los que no pertenecen) NAQ (ags)',
cluster_rec2_naq_alt= 'Clasificación por Cluster, Ítemes de NAQ (6 clases)',
turnos= 'Sistema de turnos')
BD_2020_input$educacion277<- factor(BD_2020_input$educacion277,labels=c("Sin Educacion o Basica",
"Media o Tecnica Incomp",
"Tecnica Comp o Univ Incomp",
"Universitaria Comp o Postgr"))
BD_2020_input$ocuprev_xi_am277<- factor(BD_2020_input$ocuprev_xi_am277,labels=c("Jef Intermedias",
"Prof y Tecnicos",
"Emp Oficina y Vendedores",
"Op, conductores y calif",
"No calificados"))
BD_2020_input$ing_hog_xi_am277<- factor(BD_2020_input$ing_hog_xi_am277,labels=c("Hasta 540.000 pesos",
"Desde 540.001 hasta 913.000 pesos",
"Desde 913.001 o mas" ))
BD_2020_input$vul_hm<- factor(BD_2020_input$vul_hm,labels=c( "Vulnerable Mujer", "Vulnerable Hombre"))
BD_2020_input$lf_leyman_hm<- factor(BD_2020_input$lf_leyman_hm,labels=c( "Exp a Lid LF Mujer", "Exp a Lid LF Hombre"))
BD_2020_input$la_leyman_hm<- factor(BD_2020_input$la_leyman_hm,labels=c( "Exp a Lid Aut Mujer", "Exp a Lid Aut Hombre"))
BD_2020_input$lc_leyman_hm<- factor(BD_2020_input$lc_leyman_hm,labels=c( "Exp a Lid Cons Mujer", "Exp a Lid Cons Hombre"))
BD_2020_input$depre_hm<- factor(BD_2020_input$depre_hm,labels=c( "Exp a SintDep Mujer", "Exp a SintDep Hombre"))
BD_2020_input$climasex_hm<- factor(BD_2020_input$climasex_hm,labels=c( "Exp a ClimaSex Mujer", "Exp a ClimaSex Hombre"))
BD_2020_input$k2_elevado_hm<- factor(BD_2020_input$k2_elevado_hm,labels=c("Exp a Distres Mujer", "Exp a Distres Hombre"))
BD_2020_input$psicotrop_hm<- factor(BD_2020_input$psicotrop_hm,labels=c("Exp a ConsPsic Mujer", "Exp a ConsPsic Hombre"))
BD_2020_input$jerarquia_acoso<- factor(BD_2020_input$jerarquia_acoso,labels=c("Superiores", "Companeros", "Subalternos", "Externos"))
BD_2020_input$jerarquia_acoso2<- factor(BD_2020_input$jerarquia_acoso2,labels=c("Companeros", "Subalternos", "Externos"))
#dplyr::glimpse(BD_2020_input)
#rio::export(BD_2020_input2,"BD_2020_exp.sav")
#rio::export(BD_2020_input2,"BD_2020_exp.dta")
codebook(BD_2020_input)R output from chunk distributionR output from chunk distribution
R output from chunk distribution
R output from chunk distribution
R output from chunk distribution
R output from chunk distribution
R output from chunk distribution
R output from chunk distributionknitr::asis_output(data_info)Metadata
Description
if (exists("name", meta)) {
glue::glue(
"__Dataset name__: {name}",
.envir = meta)
}Dataset name: FONDECYT_1170239 A Marzo 2020
cat(description)Prevalencia, formas de manifestacion y factores facilitadores de la violencia laboral, y sus efectos en la salud mental en poblacion asalariada en Chile atendiendo desiqualdades sociales y de genero. Inv. Responsable Elisa Ansoleaga
Metadata for search engines
Date published: 2020-03-24
Creator:Andres Gonzalez Santa Cruz
meta <- meta[setdiff(names(meta),
c("creator", "datePublished", "identifier",
"url", "citation", "spatialCoverage",
"temporalCoverage", "description", "name"))]
pander::pander(meta)- keywords: sbj_num, n_persona, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12_o1, a12_o2, a12_o3, a12_o4, a12_o5, a12_o6, a12_otra_1, a12_otra_2, a13, a14, a14_1, a15, a15_1, a16, a16_1, a17, a18_top_1, a18_top_2, a18_top_3, a18_top_4, a18_top_5, a18_top_6, a18_top_7, a18_top_8, a18_top_9, a18_top_10, a19_top_1, a19_top_2, a19_top_3, a19_top_4, a20_1_top_1, a20_1_top_2, a20_1_top_3, a20_1_top_4, a20_1_top_5, a20_1_top_6, a20_1_top_7, a20_1_top_8, a20_1_top_9, a20_1_top_10, a20_1_top_11, a20_1_top_12, a20_2_top_13, a20_2_top_14, a20_2_top_15, a20_2_top_16, a20_2_top_17, a20_2_top_18, a20_2_top_19, a20_2_top_20, a20_2_top_21, a20_2_top_22, a20_2_top_23, a20_2_top_24, a21, a21_1, a23, a25, a27, a28, a29_01, a29_02, a29_1, a22, a24, a26, a30, a31_top_1, a31_top_2, a31_top_3, a31_top_4, a31_top_5, a31_top_6, a31_top_7, a31_top_8, a31_top_9, a31_top_10, a31_top_11, a31_top_12, a31_top_13, a31_top_14, a31_top_15, a31_top_16, a32, a33_coded_1, a33_coded_2, a33_coded_3, a34_top_1, a34_top_2, a34_top_3, a34_top_4, a34_top_5, a34_top_6, a34_top_7, a34_top_8, a34_top_9, a34_top_10, a35_top_1, a35_top_2, a35_top_3, a35_top_4, a35_top_5, a35_top_6, a36, a37_1, a37_2, a37_3, a38_top_1, a38_top_2, a38_top_3, a38_top_4, a38_top_5, a38_top_6, a38_top_7, a38_top_8, a38_top_9, a38_top_10, a38_top_11, a38_top_12, a38_top_13, a38_top_14, a39_top_1, a39_top_2, a39_top_3, a39_top_4, a39_top_5, a39_top_6, a39_top_7, a40, a41, a42, a43_top_1, a43_top_2, a43_top_3, a43_top_4, a43_top_5, a44, a45_o1, a45_o2, a45_o3, a45_o4, a45_o5, a46_top_1, a46_top_12, a46_top_13, a47_top_1, a47_top_2, a47_top_3, a47_top_4, a47_top_5, a47_top_6, a47_top_7, a48, a49, a50, a51_1, a51_2, a51_3, a52, a53, a54, a56, a55, a57_1, a63, a58, a59, a60, a61, a62, weight, zona, gse, edux, ocux, edad, tamano_de_la_empresa, a1x1_coded_1, a1x2_coded_1, a11_recod, a48a49, depre, estrechez, mujer, psicotrop, frec_acos21_1, frec_acos_ecotest, testigo, k2_muyelevado, k2_elevado, k6_2, prec1, prec2, prec4, prec5, prec6, antiguedad, politrab, desempleo, trabdom, atpub, khab, kcont, latdec, latdec_dic, democ, prec8, prec9, estabilidad, prec7, democ_dic, vul1, vul2, vul3, vul4, vul5, vul6, vul7, vul, auditc, prec3r, aposoc1, aposoc2, aposoc3, aposoc4, aposoc5, aposoc6, aposoc_tot, tenpsi, iso, salgen, climasex, culorg1, culorg2, culorg3, cultura, lid_des1, lid_des2, lid_des3, lid_des4, lid_des5, lid_des6, lid_des7, lid_des8, lid_des9, lid_des10, lid_des11, lid_des12, lid_des13, lid_des14, lid_des1_rec, lid_des2_rec, lid_des3_rec, lid_des4_rec, lid_des5_rec, lid_des6_rec, lid_des7_rec, lid_des8_rec, lid_des9_rec, lid_des10_rec, lid_des11_rec, lid_des12_rec, lid_des13_rec, lid_des14_rec, lf, la, lc, naq1, naq2, naq3, naq4, naq5, naq6, naq7, naq8, naq9, naq10, naq11, naq12, naq13, naq14, naq15, naq16, naq17, naq18, naq19, naq20, naq21, naq22, naq1_rec, naq2_rec, naq3_rec, naq4_rec, naq5_rec, naq6_rec, naq7_rec, naq8_rec, naq9_rec, naq10_rec, naq11_rec, naq12_rec, naq13_rec, naq14_rec, naq15_rec, naq16_rec, naq17_rec, naq18_rec, naq19_rec, naq20_rec, naq21_rec, naq22_rec, naq_dic, naq_leyman, naq_my_e, naq_a, mas_horas, menos45, quieren_mas, prec3, prec, precr, precr1, tamano, r1, r2, r3, r4, r5, r6, r7, e1, e2, e3, esfuerzo, recompensas, la_rec, lf_rec, lc_rec, lf_leyman, la_leyman, lc_leyman, naq_sum, k6, s1, s2, s3, s4, s5, satis, ocuprev, naq_r_af_rec, naq_r_ap_rec, naq_r_rt_rec, naq_af_leyman, naq_ap_leyman, naq_rt_leyman, naq_af_my_e, naq_ap_my_e, naq_rt_my_e, naq_af_a, naq_ap_a, naq_rt_a, satlab_rec, araucaria_acoso, acoso_auto, acoso_auto_rec, naq_sum_roc, naq_sum_roc_3cat, hombre, autonaq, naq23, naq24, naq23_rec, naq24_rec, naq_r_incivismo_rec, naq_inciv_leyman, prev3, superv, ocuprev_rec, sect_priv, cont_trab, org_sind, gse_ac3, jef_hogar, agr_fisica, acos_sex, lf_rec_logit, la_rec_logit, lc_rec_logit, naq_dic_logit, lid_des_sum, lid_des_sum_cuartil, naq_sum_cuartil, expclass_culorg_1, expclass_culorg_2, expclass_culorg_3, expclass_culorg_4, expclass_culorg_5, expclass_culorg_6, cluster_rec, cluster_rec2, expclass_culorg_alt_1, expclass_culorg_alt_2, expclass_culorg_alt_3, expclass_culorg_alt_4, expclass_culorg_alt_5, expclass_culorg_alt_6, classprior1, classprior2, classprior3, classprior4, classprior5, classprior6, rec_expclass_culorg_alt_1, rec_expclass_culorg_alt_2, rec_expclass_culorg_alt_3, rec_expclass_culorg_alt_4, rec_expclass_culorg_alt_5, rec_expclass_culorg_alt_6, cluster_alt, cluster_rec_alt, rec_expclass_culorg_1, rec_expclass_culorg_2, rec_expclass_culorg_3, rec_expclass_culorg_4, rec_expclass_culorg_5, rec_expclass_culorg_6, naq_r_af_sum, naq_r_ap_sum, naq_r_rt_sum, democ_paper, lf2, lc2, lid_des_dic, lid_des_dic_logit, bajarecompensa, altoesfuerzo, desbalance, desbalance_dic, culorg1_ext, culorg2_ext, culorg3_ext, proc_res, bienst_tarea, laxo_estr, lid_des_my_e, cluster_rec2_binary, classpost_culorg_alt1, classpost_culorg_alt2, classpost_culorg_alt3, classpost_culorg_alt4, classpost_culorg_alt5, classpost_culorg_alt6, cluster_rec2_alt, naq_sum_roc3, educacion277, ocuprev_xi_am277, ing_hog_xi_am277, vul_hm, lf_leyman_hm, la_leyman_hm, lc_leyman_hm, depre_hm, psicotrop_hm, k2_elevado_hm, climasex_hm, jerarquia_acoso, jerarquia_acoso2, lid_des_leyman, acos_psico_freq, carga_dom, educacion_media, cont_def, ocuprev_nocalif, santiago, valpo, concep, classpost_naq_def_1, classpost_naq_def_2, classpost_naq_def_3, classpost_naq_def_4, classpost_naq_def_5, classpost_naq_def_6, expclass_naq_alt_1, expclass_naq_alt_2, expclass_naq_alt_3, expclass_naq_alt_4, expclass_naq_alt_5, expclass_naq_alt_6, rec_expclass_naq_alt_1, rec_expclass_naq_alt_2, rec_expclass_naq_alt_3, rec_expclass_naq_alt_4, rec_expclass_naq_alt_5, rec_expclass_naq_alt_6, cluster_naq_alt, cluster_naq_alt_rec, cluster_rec2_naq_alt and turnos
knitr::asis_output(survey_overview)Variables
if (detailed_variables || detailed_scales) {
knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}sbj_num
SbjNum
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| sbj_num | SbjNum | character | 0 | 1 | 1995 | 0 | 7 | 7 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}n_persona
A. ¿Cuántas personas de 20 o más años que trabajan en forma dependiente o asalariada, viven actualmente en esta vivienda?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| n_persona | A. ¿Cuántas personas de 20 o más años que trabajan en forma dependiente o asalariada, viven actualmente en esta vivienda? | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4, 5. 5 |
0 | 1 | 5 | 1: 1299, 2: 557, 3: 112, 4: 22 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a2
En la empresa u organización donde usted trabaja ¿Supervisa a empleados/as?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a2 | En la empresa u organización donde usted trabaja ¿Supervisa a empleados/as? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1403, S?: 583, nr: 9 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a3
¿A qué actividad económica se dedica la org. en la que trabaja?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a3 | ¿A qué actividad económica se dedica la org. en la que trabaja? | factor | FALSE | 1. Agricultur, 2. Alimentaci, 3. Armada/Fue, 4. Aseo, 5. Asesor?a, 6. Bienes inm, 7. Carga/Puer, 8. Carpinter?, 9. Centro de, 10. Comunicaci, 11. Construcci, 12. Cuidado de, 13. Deporte, 14. Distribuci, 15. Electricid, 16. Empresa de, 17. Establecim, 18. Eventos, 19. Fotograf?a, 20. Industria, 21. Inform?tic, 22. Ingenier?a, 23. Jardiner?a, 24. L?nea a?re, 25. Mantenci?n, 26. Minas y ca, 27. Multitiend, 28. nr, 29. Otros, 30. Otros come, 31. Otros serv, 32. Prestaci?n, 33. Publicidad, 34. Reparacion, 35. Restaurant, 36. Servicios, 37. Transporte, 38. Turismo |
0 | 1 | 38 | Ser: 390, Otr: 226, Con: 222, Otr: 190 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a4
¿Usted trabaja para un organismo público o para una empresa privada?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a4 | ¿Usted trabaja para un organismo público o para una empresa privada? | factor | FALSE | 1. De las Fue, 2. No sabe, 3. nr, 4. Organismo, 5. Otros, 6. Sector pri |
0 | 1 | 6 | Sec: 1623, Org: 323, Otr: 18, No : 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a5
En este trabajo ¿Tiene contrato o acuerdo de trabajo escrito?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a5 | En este trabajo ¿Tiene contrato o acuerdo de trabajo escrito? | factor | FALSE | 1. No, 2. No sabe, 3. nr, 4. S? |
0 | 1 | 4 | S?: 1806, No: 184, nr: 3, No : 2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a6
¿Qué tipo de contrato o acuerdo de trabajo tiene?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a6 | ¿Qué tipo de contrato o acuerdo de trabajo tiene? | factor | FALSE | 1. A contrata, 2. Con plazo, 3. Contrato a, 4. De deposit, 5. De palabra, 6. Indefinido, 7. No sabe, 8. No tiene c, 9. nr, 10. Otro, 11. Pago por d |
0 | 1 | 11 | Ind: 1372, Con: 333, A c: 138, Con: 75 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a7
Incluyéndose Ud ¿cuántas personas trabajan en su lugar de trabajo?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a7 | Incluyéndose Ud ¿cuántas personas trabajan en su lugar de trabajo? | factor | FALSE | 1. 200 y m?s, 2. De 5 a 10, 3. Entre 11 y, 4. Entre 50 y, 5. Menos de 5, 6. No sabe, 7. nr |
0 | 1 | 7 | Ent: 498, Ent: 435, 200: 372, Men: 341 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a8
Habitualmente en su trabajo principal, ¿cuántas hrs trabaja en la semana?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a8 | Habitualmente en su trabajo principal, ¿cuántas hrs trabaja en la semana? | factor | FALSE | 1. Jornada co, 2. Jornada ex, 3. Jornada pa, 4. No sabe (N, 5. NR (NO LEE |
0 | 1 | 5 | Jor: 1431, Jor: 366, Jor: 189, NR : 5 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a9
¿Usted desearía trabajar?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a9 | ¿Usted desearía trabajar? | factor | FALSE | 1. Est? satis, 2. M?s horas, 3. Menos hora, 4. NR (NO LEE |
0 | 1 | 4 | Est: 945, Men: 931, M?s: 114, NR : 5 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a10
Respecto a su horario de trabajo, ¿este es?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a10 | Respecto a su horario de trabajo, ¿este es? | factor | FALSE | 1. En turnos, 2. Fines de s, 3. Horario li, 4. Segun es n, 5. Solo diurn, 6. Solo noctu, 7. Variable |
0 | 1 | 7 | Sol: 1633, En : 294, Sol: 62, Fin: 2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a11
Podría decir si la remuneración de su trabajo principal, es:
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a11 | Podría decir si la remuneración de su trabajo principal, es: | factor | FALSE | 1. Es variabl, 2. Fija, 3. No sabe (N, 4. Tiene una |
0 | 1 | 4 | Fij: 1382, Tie: 407, Es : 202, No : 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_o1
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1425 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_o1 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | 1. Depende de, 2. nr |
1425 | 0.2857143 | 2 | Dep: 569, nr: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_o2
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1935 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_o2 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | 1. Depende de | 1935 | 0.0300752 | 1 | Dep: 60 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_o3
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1988 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_o3 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | 1. Depende de | 1988 | 0.0035088 | 1 | Dep: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_o4
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesariowrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}No non-missing values to show.knitr::opts_chunk$set(fig.height = old_height)1995 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_o4 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | . | 1995 | 0 | 0 | : |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_o5
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesariowrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}No non-missing values to show.knitr::opts_chunk$set(fig.height = old_height)1995 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_o5 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | . | 1995 | 0 | 0 | : |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_o6
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesariowrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}No non-missing values to show.knitr::opts_chunk$set(fig.height = old_height)1995 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_o6 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | . | 1995 | 0 | 0 | : |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_otra_1
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1954 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_otra_1 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | 1. Depende de, 2. Horas extr, 3. Imposicion, 4. Otra |
1954 | 0.0205514 | 4 | Dep: 23, Hor: 15, Otr: 2, Imp: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a12_otra_2
Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}1 unique, categorical values, so not shown.knitr::opts_chunk$set(fig.height = old_height)1994 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a12_otra_2 | Puede decirnos ¿de qué depende la parte variable de sus remuneraciones? | factor | FALSE | 1. Depende de | 1994 | 0.0005013 | 1 | Dep: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a13
¿Puede decirnos cuánto tiempo lleva trabajando en esta empresa u organización?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a13 | ¿Puede decirnos cuánto tiempo lleva trabajando en esta empresa u organización? | factor | FALSE | 1. Entre 6 me, 2. M?s de 3 a, 3. Menos de 6, 4. NR (NO LEE |
0 | 1 | 4 | M?s: 1029, Ent: 711, Men: 253, NR : 2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a14
Además del trabajo principal, ¿usted destina tiempo a otro trabajo pagado?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a14 | Además del trabajo principal, ¿usted destina tiempo a otro trabajo pagado? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1787, S?: 205, nr: 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a14_1
¿Cuántas horas le dedica a la semana en total a este otro trabajo pagado?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1790 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| a14_1 | ¿Cuántas horas le dedica a la semana en total a este otro trabajo pagado? | numeric | 1790 | 0.1027569 | 1 | 10 | 99 | 13.77073 | 15.43777 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a15
En los últimos 12 meses ¿usted ha estado desempleado?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a15 | En los últimos 12 meses ¿usted ha estado desempleado? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1750, S?: 243, nr: 2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a15_1
En los últimos 12 meses, ¿Por cuánto tiempo ha estado desempleado?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1753 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| a15_1 | En los últimos 12 meses, ¿Por cuánto tiempo ha estado desempleado? | numeric | 1753 | 0.1213033 | 0 | 3 | 12 | 4.442149 | 3.298239 | ▇▆▆▂▂ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a16
Sin considerar trabajo remunerado (semana), ¿dedica tiempo a tareas domésticas no remuneradas?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a16 | Sin considerar trabajo remunerado (semana), ¿dedica tiempo a tareas domésticas no remuneradas? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | S?: 1524, No: 466, nr: 5 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a16_1
Y aproximadamente, ¿Cuántas horas a la semana le dedica al trabajo doméstico y/o de cuidado en total?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)471 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| a16_1 | Y aproximadamente, ¿Cuántas horas a la semana le dedica al trabajo doméstico y/o de cuidado en total? | numeric | 471 | 0.7639098 | 1 | 14 | 99 | 17.13911 | 16.17969 | ▇▂▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a17
En su trabajo, ¿tiene habitualmente contacto directo con público, de viva voz o por teléfono (pacientes, clientes, alumnos, apoderados)?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a17 | En su trabajo, ¿tiene habitualmente contacto directo con público, de viva voz o por teléfono (pacientes, clientes, alumnos, apoderados)? | factor | FALSE | 1. No, 2. S? |
2 | 0.9989975 | 2 | S?: 1353, No: 640 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_1
Mi trabajo exige que yo aprenda cosas nuevas (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_1 | Mi trabajo exige que yo aprenda cosas nuevas (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
1 | 0.9994987 | 4 | De : 1052, Muy: 519, En : 318, Muy: 105 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_2
Mi trabajo exige un alto nivel de calificaciones o habilidades (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_2 | Mi trabajo exige un alto nivel de calificaciones o habilidades (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
1 | 0.9994987 | 4 | De : 1043, Muy: 544, En : 330, Muy: 77 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_3
Mi trabajo consiste en hacer siempre las mismas cosas (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_3 | Mi trabajo consiste en hacer siempre las mismas cosas (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
1 | 0.9994987 | 4 | De : 850, En : 682, Muy: 293, Muy: 169 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_4
Tengo libertad de decidir cómo hacer mi trabajo (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_4 | Tengo libertad de decidir cómo hacer mi trabajo (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
2 | 0.9989975 | 4 | De : 1074, Muy: 422, En : 420, Muy: 77 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_5
Tengo suficiente influencia en cómo ocurren las cosas en mi trabajo (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_5 | Tengo suficiente influencia en cómo ocurren las cosas en mi trabajo (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
6 | 0.9969925 | 4 | De : 1094, En : 418, Muy: 397, Muy: 80 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_6
Mi trabajo exige trabajar muy rápido (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_6 | Mi trabajo exige trabajar muy rápido (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
3 | 0.9984962 | 4 | De : 865, En : 542, Muy: 489, Muy: 96 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_7
Me piden hacer una cantidad excesiva de trabajo (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)8 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_7 | Me piden hacer una cantidad excesiva de trabajo (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
8 | 0.99599 | 4 | En : 951, De : 636, Muy: 223, Muy: 177 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_8
Tengo suficiente tiempo para hacer mi trabajo (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_8 | Tengo suficiente tiempo para hacer mi trabajo (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
6 | 0.9969925 | 4 | De : 1198, En : 377, Muy: 345, Muy: 69 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_9
Recibo demandas contradictorias por parte de otras personas (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)15 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_9 | Recibo demandas contradictorias por parte de otras personas (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
15 | 0.9924812 | 4 | En : 959, De : 512, Muy: 387, Muy: 122 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a18_top_10
Mi trabajo me exige un trabajo mental muy intenso (JCQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)8 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a18_top_10 | Mi trabajo me exige un trabajo mental muy intenso (JCQ) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des |
8 | 0.99599 | 4 | De : 868, En : 566, Muy: 425, Muy: 128 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a19_top_1
¿Hay en su trabajo momentos y/o situaciones que le producen desgaste emocional? (DEMOC)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a19_top_1 | ¿Hay en su trabajo momentos y/o situaciones que le producen desgaste emocional? (DEMOC) | factor | FALSE | 1. Algunas ve, 2. Muchas vec, 3. Nunca/casi, 4. Siempre |
1 | 0.9994987 | 4 | Alg: 915, Nun: 625, Muc: 263, Sie: 191 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a19_top_2
En general, ¿considera usted que su trabajo le produce desgaste emocional? (DEMOC)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a19_top_2 | En general, ¿considera usted que su trabajo le produce desgaste emocional? (DEMOC) | factor | FALSE | 1. Algunas ve, 2. Muchas vec, 3. Nunca/casi, 4. Siempre |
3 | 0.9984962 | 4 | Alg: 802, Nun: 715, Muc: 292, Sie: 183 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a19_top_3
En su trabajo, ¿tiene usted que guardar sus emociones y no expresarlas? (DEMOC)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a19_top_3 | En su trabajo, ¿tiene usted que guardar sus emociones y no expresarlas? (DEMOC) | factor | FALSE | 1. Algunas ve, 2. Muchas vec, 3. Nunca/casi, 4. Siempre |
7 | 0.9964912 | 4 | Nun: 759, Alg: 677, Muc: 318, Sie: 234 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a19_top_4
En su trabajo, ¿tiene usted que guardar sus opiniones y no expresarlas? (DEMOC)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a19_top_4 | En su trabajo, ¿tiene usted que guardar sus opiniones y no expresarlas? (DEMOC) | factor | FALSE | 1. Algunas ve, 2. Muchas vec, 3. Nunca/casi, 4. Siempre |
6 | 0.9969925 | 4 | Nun: 877, Alg: 675, Muc: 275, Sie: 162 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_1
Alguien le ha ocultado información que ha afectado a su rendimiento.(NAQ1)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_1 | Alguien le ha ocultado información que ha afectado a su rendimiento.(NAQ1) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1397, Alg: 421, Al : 130, A d: 24 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_2
Ha sido humillado o ridiculizado en relación a su trabajo.(NAQ2)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_2 | Ha sido humillado o ridiculizado en relación a su trabajo.(NAQ2) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1601, Alg: 286, Al : 92, A d: 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_3
Le han ordenado realizar un trabajo que está por debajo de su nivel de competencia o calificación.(NAQ3)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_3 | Le han ordenado realizar un trabajo que está por debajo de su nivel de competencia o calificación.(NAQ3) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1501, Alg: 334, Al : 133, A d: 23 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_4
Le han cambiado de realizar tareas de responsabilidad por otras más triviales o desagradables.(NAQ4)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_4 | Le han cambiado de realizar tareas de responsabilidad por otras más triviales o desagradables.(NAQ4) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1428, Alg: 413, Al : 133, A d: 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_5
Se han extendido rumores sobre usted.(NAQ5)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_5 | Se han extendido rumores sobre usted.(NAQ5) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1462, Alg: 372, Al : 102, nr: 34 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_6
Ha sido ignorado, excluido o le han dejado de hablar.(NAQ6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_6 | Ha sido ignorado, excluido o le han dejado de hablar.(NAQ6) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1635, Alg: 265, Al : 75, A d: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_7
Le han insultado u ofendido con comentarios sobre usted, sus actitudes o su vida privada.(NAQ7)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_7 | Le han insultado u ofendido con comentarios sobre usted, sus actitudes o su vida privada.(NAQ7) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1632, Alg: 278, Al : 71, A d: 9 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_8
Le han gritado o ha sido objeto de enfados espontáneos.(NAQ8)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_8 | Le han gritado o ha sido objeto de enfados espontáneos.(NAQ8) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1536, Alg: 347, Al : 94, A d: 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_9
Ha sufrido conductas intimidatorias como ser apuntado con el dedo, la invasión de su espacio personal, empujones, que no le dejen pasar, etc.(NAQ9)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_9 | Ha sufrido conductas intimidatorias como ser apuntado con el dedo, la invasión de su espacio personal, empujones, que no le dejen pasar, etc.(NAQ9) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1737, Alg: 190, Al : 55, A d: 8 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_10
Ha visto detalles o indirectas de otros que le sugieran abandonar su trabajo.(NAQ10)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_10 | Ha visto detalles o indirectas de otros que le sugieran abandonar su trabajo.(NAQ10) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1634, Alg: 258, Al : 88, A d: 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_11
Le han recordado continuamente sus errores y fallos.(NAQ11)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_11 | Le han recordado continuamente sus errores y fallos.(NAQ11) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1504, Alg: 381, Al : 91, A d: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_1_top_12
Ha sido ignorado o ha recibido una reacción hostil cuando se ha acercado a alguien.(NAQ12)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_1_top_12 | Ha sido ignorado o ha recibido una reacción hostil cuando se ha acercado a alguien.(NAQ12) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1648, Alg: 270, Al : 60, A d: 12 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_13
Ha recibido críticas persistentes sobre su trabajo y esfuerzo(NAQ13)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_13 | Ha recibido críticas persistentes sobre su trabajo y esfuerzo(NAQ13) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1367, Alg: 519, Al : 91, A d: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_14
Sus opiniones y puntos de vista han sido ignorados.(NAQ14)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_14 | Sus opiniones y puntos de vista han sido ignorados.(NAQ14) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1351, Alg: 527, Al : 97, A d: 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_15
Ha recibido bromas pesadas de gente con la que no se lleva bien.(NAQ15)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_15 | Ha recibido bromas pesadas de gente con la que no se lleva bien.(NAQ15) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1479, Alg: 406, Al : 94, A d: 12 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_16
Le han asignado tareas u objetivos inalcanzables.(NAQ16)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_16 | Le han asignado tareas u objetivos inalcanzables.(NAQ16) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1558, Alg: 333, Al : 92, A d: 9 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_17
Ha recibido acusaciones (reclamos) en su contra.(NAQ17)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_17 | Ha recibido acusaciones (reclamos) en su contra.(NAQ17) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1560, Alg: 357, Al : 71, A d: 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_18
Ha sido excesivamente supervisado en su trabajo.(NAQ18)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_18 | Ha sido excesivamente supervisado en su trabajo.(NAQ18) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1250, Alg: 418, Al : 255, A d: 68 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_19
Ha sido presionado para no reclamar algo a lo que tiene derecho (p. Ej., licencia por enfermedad, vacaciones, viáticos, etc.)(NAQ19)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_19 | Ha sido presionado para no reclamar algo a lo que tiene derecho (p. Ej., licencia por enfermedad, vacaciones, viáticos, etc.)(NAQ19) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1588, Alg: 313, Al : 77, A d: 12 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_20
Ha sido objeto de numerosas tomaduras de pelo y sarcasmos.(NAQ20)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_20 | Ha sido objeto de numerosas tomaduras de pelo y sarcasmos.(NAQ20) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1702, Alg: 231, Al : 48, A d: 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_21
Ha sido expuesto a una excesiva carga de trabajo.(NAQ21)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_21 | Ha sido expuesto a una excesiva carga de trabajo.(NAQ21) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1240, Alg: 448, Al : 239, A d: 66 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_22
Ha recibido amenazas de violencia o de abusos físicos.(NAQ22)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_22 | Ha recibido amenazas de violencia o de abusos físicos.(NAQ22) | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1826, Alg: 129, Al : 31, A d: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_23
Han dudado de su juicio sobre un asunto en el que tiene responsabilidad.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_23 | Han dudado de su juicio sobre un asunto en el que tiene responsabilidad. | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1687, Alg: 253, Al : 46, A d: 5 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a20_2_top_24
Ha sido ignorado o excluido por el resto de sus compañeros o grupo profesional.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a20_2_top_24 | Ha sido ignorado o excluido por el resto de sus compañeros o grupo profesional. | factor | FALSE | 1. A diario, 2. Al menos 1, 3. Alguna vez, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1760, Alg: 185, Al : 37, nr: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a21
Si definimos el acoso como una situación en la que una persona percibe que continuamente es objeto de conductas negativas frente a las cuales tiene dificultades para defenderse ¿según esta definición, usted ha sido acosado/a durante los últimos 6 mese
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)9 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a21 | Si definimos el acoso como una situación en la que una persona percibe que continuamente es objeto de conductas negativas frente a las cuales tiene dificultades para defenderse ¿según esta definición, usted ha sido acosado/a durante los últimos 6 mese | factor | FALSE | 1. No, 2. S? |
9 | 0.9954887 | 2 | No: 1852, S?: 134 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a21_1
Y, ¿qué tan frecuentemente ha ocurrido ese acoso?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1861 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a21_1 | Y, ¿qué tan frecuentemente ha ocurrido ese acoso? | factor | FALSE | 1. De vez en, 2. Pocas vece, 3. Pr?cticame, 4. Varias vec |
1861 | 0.0671679 | 4 | Poc: 58, De : 47, Var: 24, Pr?: 5 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a23
¿Con qué frecuencia le ha ocurrido esto?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1796 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a23 | ¿Con qué frecuencia le ha ocurrido esto? | factor | FALSE | 1. A veces, 2. Muy seguid, 3. Seguido |
1796 | 0.0997494 | 3 | A v: 152, Seg: 29, Muy: 18 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a25
¿Con qué frecuencia le ha ocurrido esto?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1975 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a25 | ¿Con qué frecuencia le ha ocurrido esto? | factor | FALSE | 1. A veces, 2. NR (NO LEE, 3. Seguido |
1975 | 0.0100251 | 3 | A v: 12, Seg: 7, NR : 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a27
¿Con qué frecuencia le ha ocurrido esto?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1968 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a27 | ¿Con qué frecuencia le ha ocurrido esto? | factor | FALSE | 1. A veces, 2. Muy seguid, 3. NR (NO LEE, 4. Seguido |
1968 | 0.0135338 | 4 | A v: 20, Seg: 4, Muy: 2, NR : 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a28
¿En los últimos 12 meses, en su trabajo principal ¿usted ha sido testigo de conductas, palabras o gestos de carácter sexual no deseado?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a28 | ¿En los últimos 12 meses, en su trabajo principal ¿usted ha sido testigo de conductas, palabras o gestos de carácter sexual no deseado? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1891, S?: 89, nr: 15 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a29_01
Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1776 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a29_01 | Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores? | factor | FALSE | 1. NR (NO LEE, 2. Personas e, 3. Sus compa?, 4. Sus subalt, 5. Sus superi |
1776 | 0.1097744 | 5 | Sus: 98, Per: 42, Sus: 41, NR : 30 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a29_02
Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1967 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a29_02 | Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores? | factor | FALSE | 1. Personas e, 2. Sus compa?, 3. Sus subalt |
1967 | 0.0140351 | 3 | Sus: 15, Per: 9, Sus: 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a29_1
¿De qué sexo han sido los agresores?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1806 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a29_1 | ¿De qué sexo han sido los agresores? | factor | FALSE | 1. De ambos s, 2. Mayoritari, 3. nr, 4. Solo hombr, 5. Solo mujer |
1806 | 0.0947368 | 5 | May: 120, De : 31, Sol: 26, Sol: 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a22
Ahora, si consideramos los últimos 12 meses, en su trabajo principal, ¿ha sido alguna vez objeto de acoso psicológico, es decir de palabras o actos repetidos que atacan su dignidad o integridad?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)9 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a22 | Ahora, si consideramos los últimos 12 meses, en su trabajo principal, ¿ha sido alguna vez objeto de acoso psicológico, es decir de palabras o actos repetidos que atacan su dignidad o integridad? | factor | FALSE | 1. No, 2. S? |
9 | 0.9954887 | 2 | No: 1786, S?: 200 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a24
¿En su trabajo principal, en los últimos 12 meses, ha sido víctima de agresión física?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a24 | ¿En su trabajo principal, en los últimos 12 meses, ha sido víctima de agresión física? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1968, S?: 20, nr: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a26
En los últimos 12 meses, en su trabajo principal ¿ha sido objeto de palabras o gestos de carácter sexual no deseado?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a26 | En los últimos 12 meses, en su trabajo principal ¿ha sido objeto de palabras o gestos de carácter sexual no deseado? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1961, S?: 27, nr: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a30
En caso de que usted haya sido objeto de alguna de las conductas señaladas, como acoso psicológico, agresión física o palabras o gestos de carácter sexual, ¿ha hecho algo para enfrentarlas?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1775 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a30 | En caso de que usted haya sido objeto de alguna de las conductas señaladas, como acoso psicológico, agresión física o palabras o gestos de carácter sexual, ¿ha hecho algo para enfrentarlas? | factor | FALSE | 1. No, 2. nr, 3. S? |
1775 | 0.1102757 | 3 | S?: 111, No: 102, nr: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_1
Evitar encontrarse con los/as agresores/as
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_1 | Evitar encontrarse con los/as agresores/as | factor | FALSE | 1. No, 2. nr, 3. S? |
1884 | 0.0556391 | 3 | S?: 60, No: 49, nr: 2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_2
Hacer uso de licencias médicas
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_2 | Hacer uso de licencias médicas | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | No: 92, S?: 19 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_3
Evitar pensar en los hechos
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_3 | Evitar pensar en los hechos | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | S?: 59, No: 52 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_4
Volcarse en otras actividades (deporte, salidas, recreación en general)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_4 | Volcarse en otras actividades (deporte, salidas, recreación en general) | factor | FALSE | 1. No, 2. nr, 3. S? |
1884 | 0.0556391 | 3 | No: 67, S?: 42, nr: 2 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_5
Renunciar a su trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_5 | Renunciar a su trabajo | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | No: 97, S?: 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_6
Buscar apoyo en las creencias religiosas o espirituales.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_6 | Buscar apoyo en las creencias religiosas o espirituales. | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | No: 88, S?: 23 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_7
Realizar planes para mejorar la situación y llevarlos a cabo.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_7 | Realizar planes para mejorar la situación y llevarlos a cabo. | factor | FALSE | 1. No, 2. nr, 3. S? |
1884 | 0.0556391 | 3 | S?: 60, No: 50, nr: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_8
Buscar información para solucionar la situación.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_8 | Buscar información para solucionar la situación. | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | S?: 68, No: 43 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_9
Comentar lo sucedido con sus compañeros/as de trabajo.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_9 | Comentar lo sucedido con sus compañeros/as de trabajo. | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | S?: 71, No: 40 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_10
Comentar lo sucedido con familiares y/o amigos/as
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_10 | Comentar lo sucedido con familiares y/o amigos/as | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | S?: 80, No: 31 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_11
Buscar ayuda en personas que sufren el mismo problema.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_11 | Buscar ayuda en personas que sufren el mismo problema. | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | No: 81, S?: 30 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_12
Hablar con el/los involucrado/s
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_12 | Hablar con el/los involucrado/s | factor | FALSE | 1. No, 2. nr, 3. S? |
1884 | 0.0556391 | 3 | S?: 74, No: 36, nr: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_13
Hablar con superiores.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_13 | Hablar con superiores. | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | S?: 86, No: 25 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_14
Denunciar los hechos al sindicato
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_14 | Denunciar los hechos al sindicato | factor | FALSE | 1. No, 2. nr, 3. S? |
1884 | 0.0556391 | 3 | No: 80, S?: 28, nr: 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_15
Tomar acciones legales
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_15 | Tomar acciones legales | factor | FALSE | 1. No, 2. S? |
1884 | 0.0556391 | 2 | No: 90, S?: 21 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a31_top_16
Otras acciones
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1884 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a31_top_16 | Otras acciones | factor | FALSE | 1. No, 2. nr, 3. S? |
1884 | 0.0556391 | 3 | No: 83, S?: 25, nr: 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a32
Y ¿usted ha vivido alguna situación en la que hayan sido vulnerados o pasados a llevar sus derechos en el trabajo?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a32 | Y ¿usted ha vivido alguna situación en la que hayan sido vulnerados o pasados a llevar sus derechos en el trabajo? | factor | FALSE | 1. No, 2. No sabe, 3. nr, 4. S? |
0 | 1 | 4 | No: 1678, S?: 268, No : 39, nr: 10 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a33_coded_1
¿Qué situación?, descríbala por favor.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1727 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a33_coded_1 | ¿Qué situación?, descríbala por favor. | factor | FALSE | 1. Abusos/ ac, 2. Agresi?n v, 3. Bajo pago, 4. Clientes p, 5. Cotizacion, 6. Discrimina, 7. En la part, 8. Estafa a l, 9. Exigencia, 10. Extenden e, 11. Inculpacio, 12. Me despidi, 13. No cumplen, 14. No le dier, 15. No pagan a, 16. No respeta, 17. No toman e, 18. No valoran, 19. NS/NC, 20. Otros, 21. Problemas, 22. Tratan mal |
1727 | 0.1343358 | 22 | Ext: 51, Pro: 30, No : 29, Tra: 18 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a33_coded_2
¿Qué situación?, descríbala por favor.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1953 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a33_coded_2 | ¿Qué situación?, descríbala por favor. | factor | FALSE | 1. Abusos/ ac, 2. Agresi?n v, 3. Clientes p, 4. Cotizacion, 5. Exigencia, 6. Extenden e, 7. Inculpacio, 8. Me despidi, 9. No cumplen, 10. No le dier, 11. No pagan a, 12. No respeta, 13. No toman e, 14. Otros, 15. Problemas, 16. Tratan mal |
1953 | 0.0210526 | 16 | Pro: 8, Ext: 6, Tra: 5, No : 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a33_coded_3
¿Qué situación?, descríbala por favor.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1988 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a33_coded_3 | ¿Qué situación?, descríbala por favor. | factor | FALSE | 1. Extenden e, 2. Me despidi, 3. No valoran, 4. Problemas |
1988 | 0.0035088 | 4 | Pro: 3, Ext: 2, Me : 1, No : 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_1
A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado (e1)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_1 | A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado (e1) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 893, En : 627, Muy: 281, Muy: 190 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_2
Me interrumpen o molestan con frecuencia en mi trabajo (e2)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_2 | Me interrumpen o molestan con frecuencia en mi trabajo (e2) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 930, Muy: 544, De : 408, Muy: 105 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_3
En el último tiempo tengo cada vez más trabajo (e3)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_3 | En el último tiempo tengo cada vez más trabajo (e3) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 819, De : 678, Muy: 255, Muy: 236 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_4
Mis superiores me dan el reconocimiento que merezco (r1)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_4 | Mis superiores me dan el reconocimiento que merezco (r1) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1051, En : 472, Muy: 267, Muy: 178 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_5
Las oportunidades de promoción en mi trabajo son escasas (r2)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_5 | Las oportunidades de promoción en mi trabajo son escasas (r2) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 824, En : 664, Muy: 231, Muy: 226 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_6
Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.) (r3)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_6 | Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.) (r3) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 773, De : 732, Muy: 306, Muy: 174 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_7
Mi estabilidad laboral es baja (r4)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_7 | Mi estabilidad laboral es baja (r4) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 995, De : 498, Muy: 389, Muy: 101 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_8
Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado (r5)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_8 | Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado (r5) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 983, En : 615, Muy: 191, Muy: 187 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_9
Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas (r6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_9 | Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas (r6) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 907, En : 686, Muy: 182, Muy: 149 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a34_top_10
Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado (r7)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a34_top_10 | Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado (r7) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 803, En : 737, Muy: 312, Muy: 123 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a35_top_1
Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a35_top_1 | Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1262, Muy: 346, En : 262, Muy: 86 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a35_top_2
Mi jefe inmediato consigue que la gente trabaje junta
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a35_top_2 | Mi jefe inmediato consigue que la gente trabaje junta | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1179, Muy: 350, En : 311, Muy: 92 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a35_top_3
Mi jefe inmediato facilita la realización del trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a35_top_3 | Mi jefe inmediato facilita la realización del trabajo | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1256, Muy: 361, En : 259, Muy: 80 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a35_top_4
Los(as) colegas con quienes trabajo se interesan o preocupan por mí
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a35_top_4 | Los(as) colegas con quienes trabajo se interesan o preocupan por mí | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1206, Muy: 376, En : 245, nr: 96 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a35_top_5
Mis colegas facilitan la realización de mi trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a35_top_5 | Mis colegas facilitan la realización de mi trabajo | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1254, Muy: 381, En : 222, nr: 80 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a35_top_6
En mi trabajo tengo la impresión de formar parte de un equipo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a35_top_6 | En mi trabajo tengo la impresión de formar parte de un equipo | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1186, Muy: 459, En : 216, nr: 68 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a36
¿En su lugar de trabajo hay un clima sexista?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a36 | ¿En su lugar de trabajo hay un clima sexista? | factor | FALSE | 1. No, 2. No sabe, 3. nr, 4. S? |
0 | 1 | 4 | No: 1738, S?: 199, No : 55, nr: 3 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a37_1
En mi empresa, (institución, organización) lo importante es cómo se hacen las cosas, no importan tanto los resultados.^^^En mi empresa, (institución, organización) lo importante son los resultados, no importa tanto cómo se hacen las cosas.
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a37_1 | En mi empresa, (institución, organización) lo importante es cómo se hacen las cosas, no importan tanto los resultados.^^^En mi empresa, (institución, organización) lo importante son los resultados, no importa tanto cómo se hacen las cosas. | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6 |
0 | 1 | 6 | 4: 449, 6: 445, 3: 388, 5: 298 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a37_2
En mi empresa, (institución, organización) se preocupan del bienestar de los trabajadores más que del cumplimiento de las tareas.^^^En mi empresa, (institución, organización) se preocupan del cumplimiento de las tareas más que del bienestar de los trabajad
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a37_2 | En mi empresa, (institución, organización) se preocupan del bienestar de los trabajadores más que del cumplimiento de las tareas.^^^En mi empresa, (institución, organización) se preocupan del cumplimiento de las tareas más que del bienestar de los trabajad | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6 |
0 | 1 | 6 | 4: 509, 3: 415, 6: 378, 5: 290 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a37_3
En mi empresa, (institución, organización) hay poca planificación y control, a veces hay que improvisar^^^En mi empresa, (institución, organización) hay una disciplina de trabajo muy estricta, todo está controlado y resulta predecible
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a37_3 | En mi empresa, (institución, organización) hay poca planificación y control, a veces hay que improvisar^^^En mi empresa, (institución, organización) hay una disciplina de trabajo muy estricta, todo está controlado y resulta predecible | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4, 5. 5, 6. 6 |
0 | 1 | 6 | 4: 485, 6: 438, 3: 393, 5: 311 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_1
¿Lo ha humillado a usted o a otros empleados/as, si no alcanzan los estándares esperados?(LID_DES1)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_1 | ¿Lo ha humillado a usted o a otros empleados/as, si no alcanzan los estándares esperados?(LID_DES1) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1636, Alg: 272, Bas: 54, Cas: 21 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_2
¿Ha incentivado las ideas o propuestas creativas o innovadoras?(LID_DES2)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_2 | ¿Ha incentivado las ideas o propuestas creativas o innovadoras?(LID_DES2) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 682, Alg: 555, Bas: 458, Cas: 277 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_3
¿Ha mostrado preocupación por los resultados?(LID_DES3)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_3 | ¿Ha mostrado preocupación por los resultados?(LID_DES3) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Bas: 597, Nun: 486, Alg: 473, Cas: 420 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_4
¿Ha hecho gestos (por ejemplo, muecas, miradas, ademanes) a usted o a otros empleados para demostrar que no está satisfecho con su esfuerzo o el de los demás?(LID_DES4)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_4 | ¿Ha hecho gestos (por ejemplo, muecas, miradas, ademanes) a usted o a otros empleados para demostrar que no está satisfecho con su esfuerzo o el de los demás?(LID_DES4) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1377, Alg: 398, Bas: 132, Cas: 72 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_5
¿Ha evitado tomar decisiones?(LID_DES5)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_5 | ¿Ha evitado tomar decisiones?(LID_DES5) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1165, Alg: 580, Bas: 131, Cas: 76 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_6
El supervisor/la supervisora ¿reconoce el buen desempeño?(LID_DES6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_6 | El supervisor/la supervisora ¿reconoce el buen desempeño?(LID_DES6) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Bas: 566, Alg: 477, Nun: 473, Cas: 452 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_7
¿Ha evitado involucrarse en su trabajo?(LID_DES7)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_7 | ¿Ha evitado involucrarse en su trabajo?(LID_DES7) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1027, Alg: 612, Bas: 181, Cas: 129 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_8
¿Ha difundido información incorrecta sobre usted o sus compañeros/as de trabajo, con el fin de dañar su posición o la de ellos en la empresa?(LID_DES8)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_8 | ¿Ha difundido información incorrecta sobre usted o sus compañeros/as de trabajo, con el fin de dañar su posición o la de ellos en la empresa?(LID_DES8) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1623, Alg: 247, Bas: 65, Cas: 32 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_9
¿Ha estado ausente cuando se le/a necesita?(LID_DES9)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_9 | ¿Ha estado ausente cuando se le/a necesita?(LID_DES9) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1246, Alg: 530, Bas: 145, Cas: 59 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_10
¿Le ha retado por teléfono, le colgó (cortó), o enviado un correo grosero porque pensó que había hecho un mal trabajo?(LID_DES10)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_10 | ¿Le ha retado por teléfono, le colgó (cortó), o enviado un correo grosero porque pensó que había hecho un mal trabajo?(LID_DES10) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 1705, Alg: 209, Bas: 60, Cas: 15 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_11
¿Busca la manera de desarrollar sus capacidades?(LID_DES11)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_11 | ¿Busca la manera de desarrollar sus capacidades?(LID_DES11) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Nun: 540, Alg: 531, Bas: 529, Cas: 373 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_12
¿Establece objetivos claros e inequívocos?(LID_DES12)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_12 | ¿Establece objetivos claros e inequívocos?(LID_DES12) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Bas: 529, Nun: 504, Alg: 490, Cas: 443 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_13
¿Define y explica claramente las tareas que le asigna a usted y a sus compañeros de trabajo?(LID_DES13)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_13 | ¿Define y explica claramente las tareas que le asigna a usted y a sus compañeros de trabajo?(LID_DES13) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Cas: 623, Bas: 600, Nun: 380, Alg: 379 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a38_top_14
¿Es flexible y está dispuesto/a a pensar de manera innovadora?(LID_DES14)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a38_top_14 | ¿Es flexible y está dispuesto/a a pensar de manera innovadora?(LID_DES14) | factor | FALSE | 1. Algunas ve, 2. Bastante a, 3. Casi siemp, 4. nr, 5. Nunca |
0 | 1 | 5 | Bas: 559, Cas: 543, Alg: 492, Nun: 378 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_1
Tiene miedo de reclamar mejores condiciones de trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_1 | Tiene miedo de reclamar mejores condiciones de trabajo | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 933, Muy: 594, De : 369, Muy: 76 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_2
Se siente indefenso/a ante el trato injusto de sus superiores
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_2 | Se siente indefenso/a ante el trato injusto de sus superiores | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 934, Muy: 651, De : 316, Muy: 72 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_3
Se siente preocupado de que le cambien las condiciones de su salario
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_3 | Se siente preocupado de que le cambien las condiciones de su salario | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 807, De : 607, Muy: 450, Muy: 117 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_4
Tiene miedo de que lo despidan si no hace lo que le piden
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_4 | Tiene miedo de que lo despidan si no hace lo que le piden | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 890, Muy: 535, De : 446, Muy: 109 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_5
Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_5 | Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 829, De : 521, Muy: 507, Muy: 115 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_6
Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_6 | Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 741, De : 604, Muy: 448, Muy: 185 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a39_top_7
Le hacen sentir que usted puede ser fácilmente reemplazado (a)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a39_top_7 | Le hacen sentir que usted puede ser fácilmente reemplazado (a) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 843, De : 496, Muy: 496, Muy: 137 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a40
¿En su empresa o institución existe alguna organización sindical o gremial?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a40 | ¿En su empresa o institución existe alguna organización sindical o gremial? | factor | FALSE | 1. No, 2. No sabe, 3. nr, 4. S? |
0 | 1 | 4 | No: 1207, S?: 712, No : 63, nr: 13 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a41
Pertenece usted a alguna organización sindical o gremial
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1283 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a41 | Pertenece usted a alguna organización sindical o gremial | factor | FALSE | 1. No, 2. nr, 3. S? |
1283 | 0.3568922 | 3 | S?: 448, No: 259, nr: 5 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a42
¿Tiene usted algún cargo de representación sindical?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1547 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a42 | ¿Tiene usted algún cargo de representación sindical? | factor | FALSE | 1. No, 2. S? |
1547 | 0.2245614 | 2 | No: 425, S?: 23 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a43_top_1
Me siento bastante satisfecho con mi trabajo actual (s1)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a43_top_1 | Me siento bastante satisfecho con mi trabajo actual (s1) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1223, Muy: 526, En : 198, Muy: 38 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a43_top_2
Casi todos los días estoy entusiasmado con mi trabajo (s2)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a43_top_2 | Casi todos los días estoy entusiasmado con mi trabajo (s2) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1181, Muy: 428, En : 326, Muy: 48 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a43_top_3
Cada día en el trabajo parece que nunca terminará (s3)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a43_top_3 | Cada día en el trabajo parece que nunca terminará (s3) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 820, De : 691, Muy: 276, Muy: 194 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a43_top_4
Disfruto con mi trabajo (s4)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a43_top_4 | Disfruto con mi trabajo (s4) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | De : 1251, Muy: 486, En : 208, Muy: 39 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a43_top_5
Considero que mi trabajo es bastante desagradable (s5)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a43_top_5 | Considero que mi trabajo es bastante desagradable (s5) | factor | FALSE | 1. De acuerdo, 2. En desacue, 3. Muy de acu, 4. Muy en des, 5. nr |
0 | 1 | 5 | En : 855, Muy: 713, De : 329, Muy: 81 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a44
En general, diría usted que su salud es…
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a44 | En general, diría usted que su salud es… | factor | FALSE | 1. Buena, 2. Excelente, 3. Mala, 4. Muy buena, 5. Muy mala, 6. NR (NO LEE |
0 | 1 | 6 | Bue: 1144, Muy: 457, Exc: 246, Mal: 137 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a45_o1
En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a45_o1 | En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones? | factor | FALSE | 1. Accidente, 2. Muerte de, 3. Ninguna (N, 4. NR (NO LEE, 5. Ser v?ctim |
0 | 1 | 5 | Nin: 1600, Mue: 223, Acc: 85, Ser: 62 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a45_o2
En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1966 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a45_o2 | En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones? | factor | FALSE | 1. Accidente, 2. Ser v?ctim |
1966 | 0.0145363 | 2 | Ser: 15, Acc: 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a45_o3
En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}1 unique, categorical values, so not shown.knitr::opts_chunk$set(fig.height = old_height)1994 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a45_o3 | En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones? | factor | FALSE | 1. Ser v?ctim | 1994 | 0.0005013 | 1 | Ser: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a45_o4
En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesariowrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}No non-missing values to show.knitr::opts_chunk$set(fig.height = old_height)1995 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a45_o4 | En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones? | factor | FALSE | . | 1995 | 0 | 0 | : |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a45_o5
En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesariowrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}No non-missing values to show.knitr::opts_chunk$set(fig.height = old_height)1995 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a45_o5 | En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones? | factor | FALSE | . | 1995 | 0 | 0 | : |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a46_top_1
¿Disminuir la ansiedad o el nerviosismo, tales como ansiolíticos? (CON_PSI)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a46_top_1 | ¿Disminuir la ansiedad o el nerviosismo, tales como ansiolíticos? (CON_PSI) | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1804, S?: 183, nr: 8 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a46_top_12
¿Ayudar a dormir, tales como hipnóticos? (CON_PSI)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a46_top_12 | ¿Ayudar a dormir, tales como hipnóticos? (CON_PSI) | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1816, S?: 170, nr: 9 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a46_top_13
¿Remontar el ánimo tales como antidepresivos? (CON_PSI)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a46_top_13 | ¿Remontar el ánimo tales como antidepresivos? (CON_PSI) | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1868, S?: 118, nr: 9 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_1
Nervioso/a (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_1 | Nervioso/a (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
4 | 0.997995 | 5 | Nun: 963, Alg: 473, Sol: 390, La : 90 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_2
Desesperado/a (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_2 | Desesperado/a (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
5 | 0.9974937 | 5 | Nun: 1575, Sol: 184, Alg: 162, La : 39 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_3
Descontrolado/a o fuera de sí (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_3 | Descontrolado/a o fuera de sí (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
7 | 0.9964912 | 5 | Nun: 1740, Sol: 114, Alg: 104, La : 22 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_4
Tan deprimido/a que nada le puede hacer sonreír (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_4 | Tan deprimido/a que nada le puede hacer sonreír (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
5 | 0.9974937 | 5 | Nun: 1580, Sol: 191, Alg: 163, La : 41 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_5
Que todo es un esfuerzo (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_5 | Que todo es un esfuerzo (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
6 | 0.9969925 | 5 | Nun: 1444, Sol: 232, Alg: 216, La : 52 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_6
Bueno/a para nada (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)9 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_6 | Bueno/a para nada (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
9 | 0.9954887 | 5 | Nun: 1788, Sol: 91, Alg: 76, La : 20 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a47_top_7
Sin esperanza (K6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a47_top_7 | Sin esperanza (K6) | factor | FALSE | 1. Algunas ve, 2. La mayor?a, 3. Nunca, 4. Siempre, 5. Solo unas |
7 | 0.9964912 | 5 | Nun: 1768, Sol: 105, Alg: 78, La : 24 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a48
Durante los últimos 12 meses, ¿se sintió triste o deprimido/a por dos semanas consecutivas o más?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a48 | Durante los últimos 12 meses, ¿se sintió triste o deprimido/a por dos semanas consecutivas o más? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1603, S?: 385, nr: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a49
Durante los últimos 12 meses, ¿llegó a sentir la pérdida de interés por la mayoría de las cosas que le gustaba hacer, tales como su trabajo, pasatiempos o cualquier actividad, por dos semanas seguidas o más?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a49 | Durante los últimos 12 meses, ¿llegó a sentir la pérdida de interés por la mayoría de las cosas que le gustaba hacer, tales como su trabajo, pasatiempos o cualquier actividad, por dos semanas seguidas o más? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1730, S?: 257, nr: 8 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a50
Si usted ha sentido alguno de los problemas de salud antes señalados, como sentirse nervioso/a o con miedo, triste, deprimido/a, o ha consumido algún medicamento ¿a qué lo/s atribuye?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)794 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a50 | Si usted ha sentido alguno de los problemas de salud antes señalados, como sentirse nervioso/a o con miedo, triste, deprimido/a, o ha consumido algún medicamento ¿a qué lo/s atribuye? | factor | FALSE | 1. A otra cir, 2. Al trabajo, 3. nr, 4. S?lo al tr |
794 | 0.602005 | 4 | A o: 473, nr: 310, S?l: 274, Al : 144 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a51_1
A continuación, quisiera hacerle algunas preguntas respecto a consumo de alcohol. ¿Qué tan seguido toma usted alguna bebida alcohólica?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a51_1 | A continuación, quisiera hacerle algunas preguntas respecto a consumo de alcohol. ¿Qué tan seguido toma usted alguna bebida alcohólica? | factor | FALSE | 1. 1 vez al m, 2. 2 a 3 vece, 3. 2 a 4 vece, 4. 4 o m?s ve, 5. Nunca |
0 | 1 | 5 | Nun: 778, 1 v: 638, 2 a: 468, 2 a: 94 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a51_2
¿Cuántos tragos suele tomar usted en un día típico de consumo de alcohol?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a51_2 | ¿Cuántos tragos suele tomar usted en un día típico de consumo de alcohol? | factor | FALSE | 1. 0 a 2 trag, 2. 10 o m?s t, 3. 3 a 4 trag, 4. 5 a 6 trag, 5. 7 a 9 trag |
0 | 1 | 5 | 0 a: 1401, 3 a: 436, 5 a: 129, 7 a: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a51_3
¿Qué tan seguido toma usted 5 o más tragos en una sola ocasión?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a51_3 | ¿Qué tan seguido toma usted 5 o más tragos en una sola ocasión? | factor | FALSE | 1. Menos de u, 2. Mensualmen, 3. Nunca, 4. Semanalmen, 5. Todos o ca |
0 | 1 | 5 | Nun: 1344, Men: 404, Men: 171, Sem: 75 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a52
Sexo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a52 | Sexo | factor | FALSE | 1. Hombre, 2. Mujer |
0 | 1 | 2 | Muj: 1024, Hom: 971 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a53
Edad (años cumplidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| a53 | Edad (años cumplidos) | numeric | 0 | 1 | 20 | 41 | 83 | 42.07769 | 12.88626 | ▇▇▆▃▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a54
Indíqueme por favor, ¿cuál es su nivel educacional?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a54 | Indíqueme por favor, ¿cuál es su nivel educacional? | factor | FALSE | 1. Educaci?n, 2. Estudios d, 3. No ha asis, 4. nr, 5. T?cnica co, 6. T?cnica in, 7. Universida |
0 | 1 | 7 | Edu: 1118, T?c: 420, Uni: 340, T?c: 80 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a56
Por favor, indíqueme el nivel educacional del jefe de hogar
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1408 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a56 | Por favor, indíqueme el nivel educacional del jefe de hogar | factor | FALSE | 1. Educaci?n, 2. Estudios d, 3. No ha asis, 4. nr, 5. T?cnica co, 6. T?cnica in, 7. Universida |
1408 | 0.2942356 | 7 | Edu: 385, T?c: 97, Uni: 80, nr: 10 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a55
¿Es usted el/la jefe/a de hogar?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a55 | ¿Es usted el/la jefe/a de hogar? | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | S?: 1408, No: 586, nr: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a57_1
Jefe de Hogar:
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1408 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a57_1 | Jefe de Hogar: | factor | FALSE | 1. Alto ejecu, 2. Ejecutivo, 3. Empleado a, 4. Estudiante, 5. Jubilado, 6. nr, 7. Obrero cal, 8. Oficio men, 9. Sin empleo, 10. Trabajos m |
1408 | 0.2942356 | 10 | Obr: 237, Emp: 172, Ofi: 83, Jub: 31 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a63
TRABAJO DEL ENTREVISTADO:
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)587 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a63 | TRABAJO DEL ENTREVISTADO: | factor | FALSE | 1. Alto ejecu, 2. Ejecutivo, 3. Empleado a, 4. nr, 5. Obrero cal, 6. Oficio men, 7. Trabajos m |
587 | 0.7057644 | 7 | Emp: 553, Obr: 550, Ofi: 186, Eje: 86 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a58
¿Nos podría decir en cuál de los siguientes tramos se encuentra su ingreso personal mensual? (considerando todos sus ingresos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a58 | ¿Nos podría decir en cuál de los siguientes tramos se encuentra su ingreso personal mensual? (considerando todos sus ingresos) | factor | FALSE | 1. De $1.000., 2. De $150.00, 3. De $350.00, 4. De $550.00, 5. De $800.00, 6. M?s de 1.5, 7. Menos de $, 8. nr |
0 | 1 | 8 | De : 548, nr: 470, De : 459, De : 313 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a59
Situación de pareja actual
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a59 | Situación de pareja actual | factor | FALSE | 1. nr, 2. Vive con p, 3. Vive sin p |
0 | 1 | 3 | Viv: 1085, Viv: 863, nr: 47 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a60
¿Cuántas personas dependen económicamente de usted (totalmente o en parte)?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a60 | ¿Cuántas personas dependen económicamente de usted (totalmente o en parte)? | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4, 6. 5, 7. 6, 8. 7, 9. 8, 10. 9, 11. 99 |
0 | 1 | 11 | 1: 575, 2: 561, 3: 335, 0: 307 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a61
Por favor, indique la frase que más se acerca a la realidad de su hogar: “Pensando en el total de ingresos mensuales de su hogar, este…
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a61 | Por favor, indique la frase que más se acerca a la realidad de su hogar: “Pensando en el total de ingresos mensuales de su hogar, este… | factor | FALSE | 1. Les alcanz, 2. No les alc, 3. NR (NO LEE |
0 | 1 | 3 | Les: 1431, No : 553, NR : 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a62
¿Me podría decir ¿en cuál de los siguientes tramos se encuentra aproximadamente el ingreso mensual total de su hogar?
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a62 | ¿Me podría decir ¿en cuál de los siguientes tramos se encuentra aproximadamente el ingreso mensual total de su hogar? | factor | FALSE | 1. De $200.00, 2. De $360.00, 3. De $540.00, 4. De $913.00, 5. M?s de $1., 6. Menos de $, 7. No sabe (N, 8. NR (NO LEE |
0 | 1 | 8 | De : 491, NR : 464, De : 445, De : 275 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}weight
Weight (ponderador)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| weight | Weight (ponderador) | numeric | 0 | 1 | 0.41 | 0.86 | 1.9 | 1.000463 | 0.5502683 | ▇▂▁▃▃ | %10.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}zona
Región (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| zona | Región (Adimark) | factor | FALSE | 1. Gran Conce, 2. Gran Santi, 3. Gran Valpa |
0 | 1 | 3 | Gra: 799, Gra: 599, Gra: 597 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}gse
GSE Encuestado/a (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| gse | GSE Encuestado/a (Adimark) | factor | FALSE | 1. ABC1, 2. C2, 3. C3, 4. d, 5. e |
0 | 1 | 5 | C3: 717, C2: 681, d: 471, ABC: 74 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}edux
Escolaridad (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| edux | Escolaridad (Adimark) | factor | FALSE | 1. Educaci?n, 2. Estudios d, 3. No ha asis, 4. NR, 5. T?cnica co, 6. Universida |
0 | 1 | 6 | Edu: 1137, T?c: 507, Uni: 253, No : 63 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ocux
Situación Ocupacional (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| ocux | Situación Ocupacional (Adimark) | factor | FALSE | 1. Alto ejecu, 2. Ejecutivo, 3. Empleado a, 4. nr, 5. Obrero cal, 6. Oficio men, 7. Trabajos m |
0 | 1 | 7 | Emp: 852, Obr: 704, Ofi: 298, Tra: 78 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}edad
Edad (en grupos) (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| edad | Edad (en grupos) (Adimark) | factor | FALSE | 1. 20 a 29, 2. 30 a 39, 3. 40 a 49, 4. 50 a 59, 5. 60 a?os y |
0 | 1 | 5 | 30 : 497, 40 : 483, 20 : 411, 50 : 398 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}tamano_de_la_empresa
Tamaño de la Empresa (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)29 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| tamano_de_la_empresa | Tamaño de la Empresa (Adimark) | factor | FALSE | 1. 1 a 10, 2. 11 a 49, 3. 200 o m?s, 4. 50 a 199 |
29 | 0.9854637 | 4 | 1 a: 661, 11 : 498, 50 : 435, 200: 372 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a1x1_coded_1
Ocupación (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a1x1_coded_1 | Ocupación (Adimark) | factor | FALSE | 1. Conductore, 2. Empleados/, 3. Jefaturas, 4. nr, 5. Operarios/, 6. Profesiona, 7. T?cnicos/a, 8. Trabajador, 9. Vendedores |
0 | 1 | 9 | Tra: 580, Ope: 296, Ven: 251, Pro: 217 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a1x2_coded_1
Cargo (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}80 unique, categorical values, so not shown.knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a1x2_coded_1 | Cargo (Adimark) | factor | FALSE | 1. Administra, 2. Analista, 3. Aseo/Auxil, 4. Asistente, 5. Atenci?n a, 6. Auxiliar, 7. Ayudante, 8. Bombero, 9. Cajera, 10. Capataz, 11. Cargadora, 12. Chofer/con, 13. Cocinero/, 14. Conserje, 15. Construcci, 16. Contador, 17. Contratist, 18. Control de, 19. Coordinado, 20. Corredora, 21. Costurera, 22. Cuidador, 23. Digitador, 24. Director, 25. Ejecutivo, 26. El?ctrico/, 27. Empaquetad, 28. Empleado, 29. Encargado, 30. Enfermeria, 31. Eventos, 32. Fiambrer?a, 33. Fonoaudiol, 34. Funcionari, 35. G?sfiter, 36. Garz?n, 37. Gerente, 38. Guardia de, 39. Hospital, 40. Ingeniero, 41. Inspector, 42. Jardinero, 43. Jefe, 44. Jefe de lo, 45. Jefe de ob, 46. Jefe de se, 47. Jornalero, 48. Junior, 49. Lavander?a, 50. Maestro ce, 51. Manicurist, 52. Manipulado, 53. Mantenimie, 54. Mec?nico, 55. Modista, 56. Mueblista, 57. Ninguno, 58. No sabe/No, 59. Nochero, 60. Obrero, 61. Oficina, 62. Operario, 63. Otros, 64. Param?dico, 65. Peluquera, 66. Pesquero, 67. Pintor/ ma, 68. Prevencion, 69. Profesiona, 70. Profesor/e, 71. Promotor, 72. Psic?loga, 73. Recepcioni, 74. Repartidor, 75. Reponedora, 76. Secretaria, 77. Servicio a, 78. Supervisor, 79. T?cnico, 80. Vendedora |
0 | 1 | 80 | Emp: 148, Ven: 146, Obr: 133, Ase: 110 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a11_recod
Podría decir si la remuneración de su trabajo principal, es: (Recodificado) (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a11_recod | Podría decir si la remuneración de su trabajo principal, es: (Recodificado) (Adimark) | factor | FALSE | 1. 1, 2. 2 |
4 | 0.997995 | 2 | 1: 1382, 2: 609 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}a48a49
Personas con síntomas depresivos (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| a48a49 | Personas con síntomas depresivos (Adimark) | factor | FALSE | 1. No, 2. nr, 3. S? |
0 | 1 | 3 | No: 1557, S?: 426, nr: 12 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}depre
Sintomatología depresiva
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)14 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| depre | Sintomatología depresiva | factor | FALSE | 1. 0, 2. 1 |
14 | 0.9929825 | 2 | 0: 1765, 1: 216 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}estrechez
Estrechez Económica
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)11 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| estrechez | Estrechez Económica | factor | FALSE | 1. 0, 2. 1 |
11 | 0.9944862 | 2 | 0: 1431, 1: 553 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}mujer
Sexo (1=Mujer)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| mujer | Sexo (1=Mujer) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1024, 0: 971 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}psicotrop
Consumo de psicotrópicos
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)10 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| psicotrop | Consumo de psicotrópicos | factor | FALSE | 1. 0, 2. 1 |
10 | 0.9949875 | 2 | 0: 1720, 1: 265 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}frec_acos21_1
Autocategorización Acoso Frecuente (Varias veces por semana y A diario)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1861 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| frec_acos21_1 | Autocategorización Acoso Frecuente (Varias veces por semana y A diario) | factor | FALSE | 1. 0, 2. 1 |
1861 | 0.0671679 | 2 | 0: 105, 1: 29 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}frec_acos_ecotest
Presencia acoso psicologico Araucaria (A22)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1795 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| frec_acos_ecotest | Presencia acoso psicologico Araucaria (A22) | factor | FALSE | 1. 0, 2. 1, 3. 99 |
1795 | 0.1002506 | 3 | 0: 152, 1: 47, 99: 1 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}testigo
Testigo de acoso sexual (A28)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)15 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| testigo | Testigo de acoso sexual (A28) | factor | FALSE | 1. 0, 2. 1 |
15 | 0.9924812 | 2 | 0: 1891, 1: 89 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}k2_muyelevado
Distrés muy elevado (>12 en suma de puntajes brutos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| k2_muyelevado | Distrés muy elevado (>12 en suma de puntajes brutos) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1927, 1: 68 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}k2_elevado
Distrés elevado o muy elevado (>6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| k2_elevado | Distrés elevado o muy elevado (>6) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1749, 1: 246 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}k6_2
Suma de puntajes brutos Distrés K6
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| k6_2 | Suma de puntajes brutos Distrés K6 | numeric | 0 | 1 | 0 | 1 | 24 | 2.565414 | 3.79041 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec1
Ausencia Contrato Escrito
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec1 | Ausencia Contrato Escrito | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1806, 1: 184 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec2
Contrato precario (Honorarios u otro)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)33 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec2 | Contrato precario (Honorarios u otro) | factor | FALSE | 1. 0, 2. 1 |
33 | 0.9834586 | 2 | 0: 1372, 1: 590 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec4
Trabaja menos de 44 hrs y desea trabajar más
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1064 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec4 | Trabaja menos de 44 hrs y desea trabajar más | factor | FALSE | 1. 0, 2. 1 |
1064 | 0.4666667 | 2 | 0: 803, 1: 128 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec5
Horario nocturno o por turnos
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec5 | Horario nocturno o por turnos | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1633, 1: 362 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec6
Remuneración variable
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec6 | Remuneración variable | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1382, 1: 613 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}antiguedad
Antigüedad (Mayor a 6 meses) (A13)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| antiguedad | Antigüedad (Mayor a 6 meses) (A13) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 1: 1740, 0: 253 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}politrab
Politrabajo (A14)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| politrab | Politrabajo (A14) | factor | FALSE | 1. 0, 2. 1 |
3 | 0.9984962 | 2 | 0: 1787, 1: 205 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}desempleo
Desempleo últimos 12 meses (A15)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| desempleo | Desempleo últimos 12 meses (A15) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1750, 1: 243 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}trabdom
Trabajo Doméstico (A16)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| trabdom | Trabajo Doméstico (A16) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 1: 1524, 0: 466 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}atpub
Atención a Público
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| atpub | Atención a Público | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 1: 1353, 0: 640 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}khab
JCQ desarrollo de habilidades
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| khab | JCQ desarrollo de habilidades | numeric | 0 | 1 | 3 | 8 | 297 | 8.534837 | 6.70343 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}kcont
JCQ control
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| kcont | JCQ control | numeric | 0 | 1 | 2 | 6 | 198 | 6.218045 | 6.908782 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}latdec
JCQ latitud decisional
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)8 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| latdec | JCQ latitud decisional | numeric | 8 | 0.99599 | 5 | 14 | 20 | 14.22597 | 2.35234 | ▁▂▇▆▂ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}latdec_dic
Baja latitud decisional (Dicotomizada)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)8 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| latdec_dic | Baja latitud decisional (Dicotomizada) | factor | FALSE | 1. 0, 2. 1 |
8 | 0.99599 | 2 | 0: 1262, 1: 725 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}democ
Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)34 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| democ | Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21) | numeric | 34 | 0.9829574 | 9 | 20 | 36 | 20.25344 | 4.912138 | ▂▇▇▂▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec8
Desempleo últimos 12 meses
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec8 | Desempleo últimos 12 meses | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1750, 1: 243 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec9
Ingreso Personal Mensual (<350000 pesos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)470 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec9 | Ingreso Personal Mensual (<350000 pesos) | factor | FALSE | 1. 0, 2. 1 |
470 | 0.764411 | 2 | 0: 1034, 1: 491 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}estabilidad
Baja estabilidad laboral
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| estabilidad | Baja estabilidad laboral | factor | FALSE | 1. 0, 2. 1 |
12 | 0.993985 | 2 | 0: 1384, 1: 599 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec7
Baja estabilidad laboral
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec7 | Baja estabilidad laboral | factor | FALSE | 1. 0, 2. 1 |
12 | 0.993985 | 2 | 0: 1384, 1: 599 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}democ_dic
Altas demandas emocionales (Dicotomizada)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)34 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| democ_dic | Altas demandas emocionales (Dicotomizada) | factor | FALSE | 1. 0, 2. 1 |
34 | 0.9829574 | 2 | 0: 1064, 1: 897 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul1
Tiene miedo de reclamar mejores condiciones de trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul1 | Tiene miedo de reclamar mejores condiciones de trabajo | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 0: 1527, 1: 445 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul2
Se siente indefenso/a ante el trato injusto de sus superiores
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)22 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul2 | Se siente indefenso/a ante el trato injusto de sus superiores | factor | FALSE | 1. 0, 2. 1 |
22 | 0.9889724 | 2 | 0: 1585, 1: 388 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul3
Se siente preocupado de que le cambien las condiciones de su salario
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)14 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul3 | Se siente preocupado de que le cambien las condiciones de su salario | factor | FALSE | 1. 0, 2. 1 |
14 | 0.9929825 | 2 | 0: 1257, 1: 724 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul4
Tiene miedo de que lo despidan si no hace lo que le piden
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)15 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul4 | Tiene miedo de que lo despidan si no hace lo que le piden | factor | FALSE | 1. 0, 2. 1 |
15 | 0.9924812 | 2 | 0: 1425, 1: 555 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul5
Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul5 | Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 0: 1336, 1: 636 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul6
Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)17 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul6 | Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan | factor | FALSE | 1. 0, 2. 1 |
17 | 0.9914787 | 2 | 0: 1189, 1: 789 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul7
Le hacen sentir que usted puede ser fácilmente reemplazado (a)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul7 | Le hacen sentir que usted puede ser fácilmente reemplazado (a) | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 0: 1339, 1: 633 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul
Vulnerabilidad, suma de puntajes (1= 3 o más)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)70 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vul | Vulnerabilidad, suma de puntajes (1= 3 o más) | factor | FALSE | 1. 0, 2. 1 |
70 | 0.9649123 | 2 | 0: 1224, 1: 701 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}auditc
Consumo riesgoso de Alcohol (ACC)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| auditc | Consumo riesgoso de Alcohol (ACC) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1484, 1: 511 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec3r
Trabaja menos de 44 hrs y desea trabajar más
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec3r | Trabaja menos de 44 hrs y desea trabajar más | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 1: 1045, 0: 945 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc1
Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista) (recodificada sin NR)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)39 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc1 | Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista) (recodificada sin NR) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
39 | 0.9804511 | 4 | 3: 1262, 4: 346, 2: 262, 1: 86 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc2
Mi jefe inmediato consigue que la gente trabaje junta (recodificada sin NR)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)63 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc2 | Mi jefe inmediato consigue que la gente trabaje junta (recodificada sin NR) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
63 | 0.9684211 | 4 | 3: 1179, 4: 350, 2: 311, 1: 92 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc3
Mi jefe inmediato facilita la realización del trabajo (recodificada sin NR)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)39 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc3 | Mi jefe inmediato facilita la realización del trabajo (recodificada sin NR) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
39 | 0.9804511 | 4 | 3: 1256, 4: 361, 2: 259, 1: 80 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc4
Los(as) colegas con quienes trabajo se interesan o preocupan por mí (recodificada sin NR)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)96 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc4 | Los(as) colegas con quienes trabajo se interesan o preocupan por mí (recodificada sin NR) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
96 | 0.9518797 | 4 | 3: 1206, 4: 376, 2: 245, 1: 72 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc5
Mis colegas facilitan la realización de mi trabajo (recodificada sin NR)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)80 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc5 | Mis colegas facilitan la realización de mi trabajo (recodificada sin NR) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
80 | 0.9598997 | 4 | 3: 1254, 4: 381, 2: 222, 1: 58 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc6
En mi trabajo tengo la impresión de formar parte de un equipo (recodificada sin NR)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)68 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc6 | En mi trabajo tengo la impresión de formar parte de un equipo (recodificada sin NR) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
68 | 0.9659148 | 4 | 3: 1186, 4: 459, 2: 216, 1: 66 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}aposoc_tot
Bajo apoyo social
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)145 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| aposoc_tot | Bajo apoyo social | factor | FALSE | 1. 0, 2. 1 |
145 | 0.9273183 | 2 | 0: 1238, 1: 612 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}tenpsi
JOBSTRAIN/Tensión Psíquica (Dicotomizada)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)40 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| tenpsi | JOBSTRAIN/Tensión Psíquica (Dicotomizada) | factor | FALSE | 1. 0, 2. 1 |
40 | 0.9799499 | 2 | 0: 1641, 1: 314 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}iso
ISOSTRAIN
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)173 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| iso | ISOSTRAIN | factor | FALSE | 1. 0, 2. 1 |
173 | 0.9132832 | 2 | 0: 1648, 1: 174 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}salgen
Salud General (1=Mala o Muy Mala)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| salgen | Salud General (1=Mala o Muy Mala) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1847, 1: 146 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}climasex
Clima Sexista
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)58 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| climasex | Clima Sexista | factor | FALSE | 1. 0, 2. 1 |
58 | 0.9709273 | 2 | 0: 1738, 1: 199 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}culorg1
CO orientada a Resultados (vs. a Procesos) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| culorg1 | CO orientada a Resultados (vs. a Procesos) (Art JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1192, 0: 803 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}culorg2
CO orientada a Cumplimiento de Tareas (vs. Bienestar de Trabajadores) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| culorg2 | CO orientada a Cumplimiento de Tareas (vs. Bienestar de Trabajadores) (Art JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1177, 0: 818 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}culorg3
CO orientada a Control Estricto (vs. Laxo) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| culorg3 | CO orientada a Control Estricto (vs. Laxo) (Art JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1234, 0: 761 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cultura
C.O. Orientada a resultados, tareas y rígida
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cultura | C.O. Orientada a resultados, tareas y rígida | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1346, 1: 649 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des1
Variable LID_DES1 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des1 | Variable LID_DES1 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
12 | 0.993985 | 4 | 0: 1636, 1: 272, 2: 54, 3: 21 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des2
Variable LID_DES2 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des2 | Variable LID_DES2 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
23 | 0.9884712 | 4 | 0: 682, 1: 555, 2: 458, 3: 277 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des3
Variable LID_DES3 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)19 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des3 | Variable LID_DES3 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
19 | 0.9904762 | 4 | 1: 597, 3: 486, 2: 473, 0: 420 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des4
Variable LID_DES4 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)16 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des4 | Variable LID_DES4 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
16 | 0.9919799 | 4 | 0: 1377, 1: 398, 2: 132, 3: 72 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des5
Variable LID_DES5 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)43 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des5 | Variable LID_DES5 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
43 | 0.9784461 | 4 | 0: 1165, 1: 580, 2: 131, 3: 76 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des6
Variable LID_DES6 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)27 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des6 | Variable LID_DES6 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
27 | 0.9864662 | 4 | 2: 566, 1: 477, 0: 473, 3: 452 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des7
Variable LID_DES7 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)46 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des7 | Variable LID_DES7 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
46 | 0.9769424 | 4 | 0: 1027, 1: 612, 2: 181, 3: 129 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des8
Variable LID_DES8 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)28 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des8 | Variable LID_DES8 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
28 | 0.9859649 | 4 | 0: 1623, 1: 247, 2: 65, 3: 32 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des9
Variable LID_DES9 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)15 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des9 | Variable LID_DES9 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
15 | 0.9924812 | 4 | 0: 1246, 1: 530, 2: 145, 3: 59 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des10
Variable LID_DES10 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des10 | Variable LID_DES10 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
6 | 0.9969925 | 4 | 0: 1705, 1: 209, 2: 60, 3: 15 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des11
Variable LID_DES11 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)22 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des11 | Variable LID_DES11 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
22 | 0.9889724 | 4 | 0: 540, 1: 531, 2: 529, 3: 373 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des12
Variable LID_DES12 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)29 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des12 | Variable LID_DES12 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
29 | 0.9854637 | 4 | 2: 529, 0: 504, 1: 490, 3: 443 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des13
Variable LID_DES13 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)13 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des13 | Variable LID_DES13 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
13 | 0.9934837 | 4 | 3: 623, 2: 600, 0: 380, 1: 379 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des14
Variable LID_DES14 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des14 | Variable LID_DES14 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
23 | 0.9884712 | 4 | 2: 559, 3: 543, 1: 492, 0: 378 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des1_rec
Variable LID_DES1 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des1_rec | Variable LID_DES1 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
12 | 0.993985 | 2 | 0: 1908, 1: 75 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des2_rec
Variable LID_DES2 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des2_rec | Variable LID_DES2 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 0: 1237, 1: 735 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des3_rec
Variable LID_DES3 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)19 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des3_rec | Variable LID_DES3 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
19 | 0.9904762 | 2 | 0: 1017, 1: 959 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des4_rec
Variable LID_DES4 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)16 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des4_rec | Variable LID_DES4 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
16 | 0.9919799 | 2 | 0: 1775, 1: 204 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des5_rec
Variable LID_DES5 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)43 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des5_rec | Variable LID_DES5 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
43 | 0.9784461 | 2 | 0: 1745, 1: 207 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des6_rec
Variable LID_DES6 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)27 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des6_rec | Variable LID_DES6 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
27 | 0.9864662 | 2 | 1: 1018, 0: 950 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des7_rec
Variable LID_DES7 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)46 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des7_rec | Variable LID_DES7 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
46 | 0.9769424 | 2 | 0: 1639, 1: 310 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des8_rec
Variable LID_DES8 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)28 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des8_rec | Variable LID_DES8 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
28 | 0.9859649 | 2 | 0: 1870, 1: 97 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des9_rec
Variable LID_DES9 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)15 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des9_rec | Variable LID_DES9 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
15 | 0.9924812 | 2 | 0: 1776, 1: 204 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des10_rec
Variable LID_DES10 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des10_rec | Variable LID_DES10 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
6 | 0.9969925 | 2 | 0: 1914, 1: 75 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des11_rec
Variable LID_DES11 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)22 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des11_rec | Variable LID_DES11 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
22 | 0.9889724 | 2 | 0: 1071, 1: 902 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des12_rec
Variable LID_DES12 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)29 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des12_rec | Variable LID_DES12 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
29 | 0.9854637 | 2 | 0: 994, 1: 972 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des13_rec
Variable LID_DES13 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)13 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des13_rec | Variable LID_DES13 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
13 | 0.9934837 | 2 | 1: 1223, 0: 759 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des14_rec
Variable LID_DES14 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des14_rec | Variable LID_DES14 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 1: 1102, 0: 870 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lf
Suma de puntajes brutos Liderazgo Laissez-Faire (todas las variables)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| lf | Suma de puntajes brutos Liderazgo Laissez-Faire (todas las variables) | numeric | 0 | 1 | 0 | 3 | 12 | 3.222556 | 1.959109 | ▆▇▃▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}la
Suma de puntajes brutos Liderazgo Autoritario
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| la | Suma de puntajes brutos Liderazgo Autoritario | numeric | 0 | 1 | 0 | 0 | 12 | 1.086717 | 1.771274 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lc
Suma de puntajes brutos Liderazgo Constructivo (todas las variables)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| lc | Suma de puntajes brutos Liderazgo Constructivo (todas las variables) | numeric | 0 | 1 | 0 | 9 | 18 | 8.791479 | 5.054927 | ▆▇▆▇▅ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq1
Variable NAQ1 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq1 | Variable NAQ1 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
23 | 0.9884712 | 5 | 0: 1397, 1: 421, 2: 90, 3: 40 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq2
Variable NAQ2 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq2 | Variable NAQ2 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
2 | 0.9989975 | 5 | 0: 1601, 1: 286, 2: 64, 3: 28 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq3
Variable NAQ3 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq3 | Variable NAQ3 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1501, 1: 334, 2: 90, 3: 43 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq4
Variable NAQ4 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq4 | Variable NAQ4 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1428, 1: 413, 2: 102, 3: 31 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq5
Variable NAQ5 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)34 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq5 | Variable NAQ5 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
34 | 0.9829574 | 5 | 0: 1462, 1: 372, 2: 75, 3: 27 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq6
Variable NAQ6 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq6 | Variable NAQ6 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1635, 1: 265, 2: 51, 3: 24 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq7
Variable NAQ7 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq7 | Variable NAQ7 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
5 | 0.9974937 | 5 | 0: 1632, 1: 278, 2: 45, 3: 26 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq8
Variable NAQ8 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq8 | Variable NAQ8 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1536, 1: 347, 2: 73, 3: 21 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq9
Variable NAQ9 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq9 | Variable NAQ9 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
5 | 0.9974937 | 5 | 0: 1737, 1: 190, 2: 37, 3: 18 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq10
Variable NAQ10 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq10 | Variable NAQ10 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1634, 1: 258, 2: 63, 3: 25 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq11
Variable NAQ11 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq11 | Variable NAQ11 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
3 | 0.9984962 | 5 | 0: 1504, 1: 381, 2: 70, 3: 21 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq12
Variable NAQ12 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq12 | Variable NAQ12 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
5 | 0.9974937 | 5 | 0: 1648, 1: 270, 2: 37, 3: 23 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq13
Variable NAQ13 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq13 | Variable NAQ13 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
2 | 0.9989975 | 5 | 0: 1367, 1: 519, 2: 72, 3: 19 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq14
Variable NAQ14 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq14 | Variable NAQ14 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
3 | 0.9984962 | 5 | 0: 1351, 1: 527, 2: 67, 3: 30 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq15
Variable NAQ15 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq15 | Variable NAQ15 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1479, 1: 406, 2: 70, 3: 24 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq16
Variable NAQ16 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq16 | Variable NAQ16 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
3 | 0.9984962 | 5 | 0: 1558, 1: 333, 2: 69, 3: 23 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq17
Variable NAQ17 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq17 | Variable NAQ17 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
3 | 0.9984962 | 5 | 0: 1560, 1: 357, 2: 54, 3: 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq18
Variable NAQ18 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq18 | Variable NAQ18 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1250, 1: 418, 2: 161, 3: 94 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq19
Variable NAQ19 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq19 | Variable NAQ19 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
5 | 0.9974937 | 5 | 0: 1588, 1: 313, 2: 60, 3: 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq20
Variable NAQ20 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq20 | Variable NAQ20 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
3 | 0.9984962 | 5 | 0: 1702, 1: 231, 2: 32, 3: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq21
Variable NAQ21 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq21 | Variable NAQ21 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
2 | 0.9989975 | 5 | 0: 1240, 1: 448, 2: 132, 3: 107 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq22
Variable NAQ22 recodificada (sin datos perdidos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq22 | Variable NAQ22 recodificada (sin datos perdidos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
2 | 0.9989975 | 5 | 0: 1826, 1: 129, 2: 24, 3: 7 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq1_rec
Variable NAQ1 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq1_rec | Variable NAQ1 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 0: 1908, 1: 64 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq2_rec
Variable NAQ2 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq2_rec | Variable NAQ2 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1951, 1: 42 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq3_rec
Variable NAQ3 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq3_rec | Variable NAQ3 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1925, 1: 66 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq4_rec
Variable NAQ4 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq4_rec | Variable NAQ4 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1943, 1: 48 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq5_rec
Variable NAQ5 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)34 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq5_rec | Variable NAQ5 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
34 | 0.9829574 | 2 | 0: 1909, 1: 52 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq6_rec
Variable NAQ6 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq6_rec | Variable NAQ6 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1951, 1: 40 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq7_rec
Variable NAQ7 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq7_rec | Variable NAQ7 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1955, 1: 35 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq8_rec
Variable NAQ8 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq8_rec | Variable NAQ8 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1956, 1: 35 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq9_rec
Variable NAQ9 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq9_rec | Variable NAQ9 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1964, 1: 26 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq10_rec
Variable NAQ10 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq10_rec | Variable NAQ10 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1955, 1: 36 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq11_rec
Variable NAQ11 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq11_rec | Variable NAQ11 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
3 | 0.9984962 | 2 | 0: 1955, 1: 37 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq12_rec
Variable NAQ12 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq12_rec | Variable NAQ12 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1955, 1: 35 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq13_rec
Variable NAQ13 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq13_rec | Variable NAQ13 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1958, 1: 35 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq14_rec
Variable NAQ14 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq14_rec | Variable NAQ14 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
3 | 0.9984962 | 2 | 0: 1945, 1: 47 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq15_rec
Variable NAQ15 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq15_rec | Variable NAQ15 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1955, 1: 36 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq16_rec
Variable NAQ16 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq16_rec | Variable NAQ16 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
3 | 0.9984962 | 2 | 0: 1960, 1: 32 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq17_rec
Variable NAQ17 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq17_rec | Variable NAQ17 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
3 | 0.9984962 | 2 | 0: 1971, 1: 21 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq18_rec
Variable NAQ18 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq18_rec | Variable NAQ18 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1829, 1: 162 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq19_rec
Variable NAQ19 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq19_rec | Variable NAQ19 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1961, 1: 29 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq20_rec
Variable NAQ20 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq20_rec | Variable NAQ20 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
3 | 0.9984962 | 2 | 0: 1965, 1: 27 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq21_rec
Variable NAQ21 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq21_rec | Variable NAQ21 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1820, 1: 173 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq22_rec
Variable NAQ22 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq22_rec | Variable NAQ22 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
2 | 0.9989975 | 2 | 0: 1979, 1: 14 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_dic
Suma puntajes dicotomizado (NAQ) (Dichotomized sum score)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| naq_dic | Suma puntajes dicotomizado (NAQ) (Dichotomized sum score) | numeric | 0 | 1 | 0 | 0 | 22 | 0.5473684 | 1.702301 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_leyman
NAQ Criterio Leymann
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_leyman | NAQ Criterio Leymann | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1573, 1: 422 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_my_e
NAQ Criterio Mikkelsen y Einarsen
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_my_e | NAQ Criterio Mikkelsen y Einarsen | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1797, 1: 198 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_a
NAQ Criterio Agervold
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_a | NAQ Criterio Agervold | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1870, 1: 125 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}mas_horas
Desearía trabajar más horas a la semana (A9)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| mas_horas | Desearía trabajar más horas a la semana (A9) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1876, 1: 114 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}menos45
Trabajo principal de menos 45 horas (A8)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)9 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| menos45 | Trabajo principal de menos 45 horas (A8) | factor | FALSE | 1. 0, 2. 1 |
9 | 0.9954887 | 2 | 0: 1620, 1: 366 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}quieren_mas
Desearía trabajar más horas a la semana (A9)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| quieren_mas | Desearía trabajar más horas a la semana (A9) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1876, 1: 114 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec3
Trabaja menos de 45 horas semanales y desearia trabajar más
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec3 | Trabaja menos de 45 horas semanales y desearia trabajar más | factor | FALSE | 1. 0, 2. 1 |
12 | 0.993985 | 2 | 0: 1923, 1: 60 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prec
Indicador de precariedad
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)514 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prec | Indicador de precariedad | factor | FALSE | 1. 0, 2. 1 |
514 | 0.7423559 | 2 | 0: 1140, 1: 341 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}precr
Indicador de precariedad
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)61 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| precr | Indicador de precariedad | factor | FALSE | 1. 0, 2. 1 |
61 | 0.9694236 | 2 | 0: 1625, 1: 309 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}precr1
3 o más indicadores de precariedad laboral (de suma prec1 2 3 5 6 7 8 y vul)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)119 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| precr1 | 3 o más indicadores de precariedad laboral (de suma prec1 2 3 5 6 7 8 y vul) | factor | FALSE | 1. 0, 2. 1 |
119 | 0.9403509 | 2 | 0: 1423, 1: 453 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}tamano
Tamaño de la organización (Adimark)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)29 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| tamano | Tamaño de la organización (Adimark) | factor | FALSE | 1. 0, 2. 1 |
29 | 0.9854637 | 2 | 1: 1159, 0: 807 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r1
Mis superiores me dan el reconocimiento que merezco
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)27 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r1 | Mis superiores me dan el reconocimiento que merezco | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
27 | 0.9864662 | 4 | 3: 1051, 2: 472, 4: 267, 1: 178 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r2
Las oportunidades de promoción en mi trabajo son escasas
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)50 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r2 | Las oportunidades de promoción en mi trabajo son escasas | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
50 | 0.9749373 | 4 | 2: 824, 3: 664, 1: 231, 4: 226 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r3
Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)10 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r3 | Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
10 | 0.9949875 | 4 | 3: 773, 2: 732, 4: 306, 1: 174 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r4
Mi estabilidad laboral es baja
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r4 | Mi estabilidad laboral es baja | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
12 | 0.993985 | 4 | 3: 995, 2: 498, 4: 389, 1: 101 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r5
Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)19 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r5 | Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
19 | 0.9904762 | 4 | 3: 983, 2: 615, 4: 191, 1: 187 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r6
Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)71 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r6 | Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
71 | 0.964411 | 4 | 3: 907, 2: 686, 1: 182, 4: 149 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}r7
Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)20 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| r7 | Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
20 | 0.9899749 | 4 | 3: 803, 2: 737, 1: 312, 4: 123 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}e1
A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| e1 | A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
4 | 0.997995 | 4 | 3: 893, 2: 627, 4: 281, 1: 190 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}e2
Me interrumpen o molestan con frecuencia en mi trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)8 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| e2 | Me interrumpen o molestan con frecuencia en mi trabajo | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
8 | 0.99599 | 4 | 2: 930, 1: 544, 3: 408, 4: 105 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}e3
En el último tiempo tengo cada vez más trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| e3 | En el último tiempo tengo cada vez más trabajo | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
7 | 0.9964912 | 4 | 2: 819, 3: 678, 1: 255, 4: 236 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}esfuerzo
Suma Esfuerzos
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)10 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| esfuerzo | Suma Esfuerzos | numeric | 10 | 0.9949875 | 3 | 7 | 12 | 7.118892 | 1.990646 | ▂▇▇▃▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}recompensas
Suma Recompensas
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)136 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| recompensas | Suma Recompensas | numeric | 136 | 0.9318296 | 7 | 18 | 28 | 18.10382 | 3.124176 | ▁▂▇▃▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}la_rec
Suma ptjes dicotomizados Leyman (1,4,8,10) Lid Autoritario
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| la_rec | Suma ptjes dicotomizados Leyman (1,4,8,10) Lid Autoritario | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
0 | 1 | 5 | 0: 1691, 1: 213, 2: 49, 3: 28 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lf_rec
Suma ptjes dicotomizados Leyman (5,7,9) Lid Laissez-Faire
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lf_rec | Suma ptjes dicotomizados Leyman (5,7,9) Lid Laissez-Faire | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
0 | 1 | 4 | 0: 1465, 1: 380, 2: 109, 3: 41 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lc_rec
Suma ptjes dicotomizados Leyman (6,11,12,14) Lid Constructivo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lc_rec | Suma ptjes dicotomizados Leyman (6,11,12,14) Lid Constructivo | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
0 | 1 | 5 | 0: 553, 4: 515, 3: 366, 1: 286 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lf_leyman
Exposición a Lid. Laissez-Faire (Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lf_leyman | Exposición a Lid. Laissez-Faire (Leymann) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1465, 1: 530 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}la_leyman
Exposición a Lid. Autoritario/Tiránico (Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| la_leyman | Exposición a Lid. Autoritario/Tiránico (Leymann) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1691, 1: 304 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lc_leyman
Exposición a Lid. Constructivo (Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lc_leyman | Exposición a Lid. Constructivo (Leymann) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1442, 0: 553 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_sum
Acosados de vez en cuando o víctimas de acoso (Puntaje Bruto NAQ)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| naq_sum | Acosados de vez en cuando o víctimas de acoso (Puntaje Bruto NAQ) | numeric | 0 | 1 | 0 | 3 | 88 | 7.147368 | 9.725951 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}k6
Distrés elevado o muy elevado (>6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| k6 | Distrés elevado o muy elevado (>6) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1749, 1: 246 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}s1
Me siento bastante satisfecho con mi trabajo actual
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)10 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| s1 | Me siento bastante satisfecho con mi trabajo actual | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
10 | 0.9949875 | 4 | 3: 1223, 4: 526, 2: 198, 1: 38 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}s2
Casi todos los días estoy entusiasmado con mi trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)12 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| s2 | Casi todos los días estoy entusiasmado con mi trabajo | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
12 | 0.993985 | 4 | 3: 1181, 4: 428, 2: 326, 1: 48 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}s3
Cada día en el trabajo parece que nunca terminará
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)14 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| s3 | Cada día en el trabajo parece que nunca terminará | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
14 | 0.9929825 | 4 | 3: 820, 2: 691, 4: 276, 1: 194 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}s4
Disfruto con mi trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)11 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| s4 | Disfruto con mi trabajo | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
11 | 0.9944862 | 4 | 3: 1251, 4: 486, 2: 208, 1: 39 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}s5
Considero que mi trabajo es bastante desagradable (invertido)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)17 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| s5 | Considero que mi trabajo es bastante desagradable (invertido) | factor | FALSE | 1. 1, 2. 2, 3. 3, 4. 4 |
17 | 0.9914787 | 4 | 3: 855, 4: 713, 2: 329, 1: 81 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}satis
Suma de Puntajes Brutos Satisfacción Laboral
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| satis | Suma de Puntajes Brutos Satisfacción Laboral | numeric | 0 | 1 | 0 | 15 | 20 | 14.84211 | 2.679397 | ▁▁▂▇▃ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ocuprev
Ocupación Araucaria (codificada erroneamente, ver ocuprev rec)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| ocuprev | Ocupación Araucaria (codificada erroneamente, ver ocuprev rec) | factor | FALSE | 1. Conductores transporte y Operadores m�quinas, 2. Empleados/as de Oficina, 3. Jefaturas Intermedias, 4. Operarios/as, 5. Profesionales, 6. T�cnicos/as, 7. Trabajadores de los servicios, 8. Trabajadores no calificados, 9. Vendedores |
4 | 0.997995 | 9 | Tra: 372, Ope: 319, Emp: 246, Ven: 223 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_af_rec
Suma de puntajes dic (Al menos una vez a la semana) Acoso Físico
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_r_af_rec | Suma de puntajes dic (Al menos una vez a la semana) Acoso Físico | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
0 | 1 | 4 | 0: 1940, 1: 39, 2: 12, 3: 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_ap_rec
Suma de puntajes dic (Al menos una vez a la semana) Acoso Psicológico
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_r_ap_rec | Suma de puntajes dic (Al menos una vez a la semana) Acoso Psicológico | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4, 6. 5, 7. 6, 8. 7, 9. 8, 10. 9, 11. 10, 12. 11, 13. 12 |
0 | 1 | 13 | 0: 1814, 1: 91, 2: 34, 3: 22 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_rt_rec
Suma de puntajes dic (Al menos una vez a la semana) Rel con Trabajo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_r_rt_rec | Suma de puntajes dic (Al menos una vez a la semana) Rel con Trabajo | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4, 6. 5, 7. 6, 8. 7 |
0 | 1 | 8 | 0: 1635, 1: 233, 2: 76, 3: 30 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_af_leyman
NAQ Acoso Físico (Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_af_leyman | NAQ Acoso Físico (Leymann) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1940, 1: 55 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_ap_leyman
NAQ Acoso Psicológico (Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_ap_leyman | NAQ Acoso Psicológico (Leymann) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1814, 1: 181 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_rt_leyman
NAQ Relacionado con el Trabajo (Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_rt_leyman | NAQ Relacionado con el Trabajo (Leymann) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1635, 1: 360 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_af_my_e
NAQ Acoso Físico (Mikkelsen y Einarsen)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_af_my_e | NAQ Acoso Físico (Mikkelsen y Einarsen) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1979, 1: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_ap_my_e
NAQ Acoso Psicológico (Mikkelsen y Einarsen)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_ap_my_e | NAQ Acoso Psicológico (Mikkelsen y Einarsen) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1905, 1: 90 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_rt_my_e
NAQ Relacionado con el Trabajo (Mikkelsen y Einarsen)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_rt_my_e | NAQ Relacionado con el Trabajo (Mikkelsen y Einarsen) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1868, 1: 127 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_af_a
NAQ Acoso Físico (Agervold)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_af_a | NAQ Acoso Físico (Agervold) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1991, 1: 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_ap_a
NAQ Acoso Psicológico (Agervold)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_ap_a | NAQ Acoso Psicológico (Agervold) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1939, 1: 56 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_rt_a
NAQ Relacionado con el Trabajo (Agervold)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_rt_a | NAQ Relacionado con el Trabajo (Agervold) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1944, 1: 51 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}satlab_rec
Baja Satisfacción Laboral (1er cuartil suma de puntajes)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| satlab_rec | Baja Satisfacción Laboral (1er cuartil suma de puntajes) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1469, 1: 526 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}araucaria_acoso
Suma acoso sexual, psicológico y físico, Araucaria
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| araucaria_acoso | Suma acoso sexual, psicológico y físico, Araucaria | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1969, 1: 26 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}acoso_auto
Suma acoso sexual, psicológico y físico, Araucaria, más Autocategorización NAQ
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| acoso_auto | Suma acoso sexual, psicológico y físico, Araucaria, más Autocategorización NAQ | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
0 | 1 | 5 | 0: 1751, 1: 124, 2: 105, 3: 13 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}acoso_auto_rec
Al menos una forma de victimización acoso sexual, físico, psi o autonaq
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| acoso_auto_rec | Al menos una forma de victimización acoso sexual, físico, psi o autonaq | factor | FALSE | 1. Expuesto, 2. No expuest |
0 | 1 | 2 | No : 1751, Exp: 244 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_sum_roc
Variable Dicotomizada Suma Puntajes Brutos NAQ Curvas ROC (>11 ptos) (tesis ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_sum_roc | Variable Dicotomizada Suma Puntajes Brutos NAQ Curvas ROC (>11 ptos) (tesis ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1579, 1: 416 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_sum_roc_3cat
Diferencia entre quienes obtienen >11 ptos, >9 ptos, y 0 a 8 (tesis ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_sum_roc_3cat | Diferencia entre quienes obtienen >11 ptos, >9 ptos, y 0 a 8 (tesis ags) | factor | FALSE | 1. 0, 2. 1, 3. 2 |
0 | 1 | 3 | 0: 1406, 2: 416, 1: 173 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}hombre
Sexo Hombre (1=Hombre)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| hombre | Sexo Hombre (1=Hombre) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1024, 1: 971 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}autonaq
Autocategorización según Definición NAQ (A21)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)9 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| autonaq | Autocategorización según Definición NAQ (A21) | factor | FALSE | 1. 0, 2. 1 |
9 | 0.9954887 | 2 | 0: 1852, 1: 134 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq23
Variable NAQ23 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq23 | Variable NAQ23 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
4 | 0.997995 | 5 | 0: 1687, 1: 253, 2: 35, 3: 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq24
Variable NAQ24 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq24 | Variable NAQ24 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
7 | 0.9964912 | 5 | 0: 1760, 1: 185, 2: 26, 3: 11 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq23_rec
Variable NAQ23 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq23_rec | Variable NAQ23 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1975, 1: 16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq24_rec
Variable NAQ24 recodificada (sin datos perdidos en base a criterio Leymann)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq24_rec | Variable NAQ24 recodificada (sin datos perdidos en base a criterio Leymann) | factor | FALSE | 1. 0, 2. 1 |
7 | 0.9964912 | 2 | 0: 1971, 1: 17 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_incivismo_rec
Escala Incivismo NAQ (Suma items 7, 14, 23 y 24) (Am y Xi 2018)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_r_incivismo_rec | Escala Incivismo NAQ (Suma items 7, 14, 23 y 24) (Am y Xi 2018) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 |
0 | 1 | 5 | 0: 1912, 1: 63, 2: 12, 3: 4 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_inciv_leyman
Escala Incivismo NAQ (>0) (Am y Xi 2018)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_inciv_leyman | Escala Incivismo NAQ (>0) (Am y Xi 2018) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1912, 1: 83 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}prev3
Ocupación Araucaria (Operarios, Técnicos y No Calificados v/s Oficinistas, Servicios, Jefaturas y Profesionales) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| prev3 | Ocupación Araucaria (Operarios, Técnicos y No Calificados v/s Oficinistas, Servicios, Jefaturas y Profesionales) (Art JPT) | factor | FALSE | 1. 1, 2. 2 |
4 | 0.997995 | 2 | 2: 1009, 1: 982 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}superv
Supervisa a otros
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)9 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| superv | Supervisa a otros | factor | FALSE | 1. 0, 2. 1 |
9 | 0.9954887 | 2 | 0: 1403, 1: 583 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ocuprev_rec
Ocupación Araucaria (Recodificada 5= Vendedores) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| ocuprev_rec | Ocupación Araucaria (Recodificada 5= Vendedores) (Art JPT) | factor | FALSE | 1. Conductores transporte y Operadores m�quinas, 2. Empleados/as de Oficina, 3. Jefaturas Intermedias, 4. Operarios/as, 5. Profesionales, 6. T�cnicos/as, 7. Trabajadores de los servicios, 8. Trabajadores no calificados, 9. Vendedores |
4 | 0.997995 | 9 | Tra: 372, Ope: 319, Emp: 246, Ven: 223 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sect_priv
Trabaja Organismo Público o Empresa Privada (1=Sector privado)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)23 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| sect_priv | Trabaja Organismo Público o Empresa Privada (1=Sector privado) | factor | FALSE | 1. 0, 2. 1 |
23 | 0.9884712 | 2 | 1: 1623, 0: 349 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cont_trab
Ausencia de Contrato Escrito (1= Sin Contrato)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cont_trab | Ausencia de Contrato Escrito (1= Sin Contrato) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 0: 1806, 1: 184 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}org_sind
Existencia de Organización Sindical o Gremial (1=No)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)76 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| org_sind | Existencia de Organización Sindical o Gremial (1=No) | factor | FALSE | 1. 0, 2. 1 |
76 | 0.9619048 | 2 | 1: 1207, 0: 712 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}gse_ac3
GSE Encuestado (D y E) (1= DyE)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| gse_ac3 | GSE Encuestado (D y E) (1= DyE) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1472, 1: 523 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}jef_hogar
Jefe de Hogar (1=Sí)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| jef_hogar | Jefe de Hogar (1=Sí) | factor | FALSE | 1. 0, 2. 1 |
1 | 0.9994987 | 2 | 1: 1408, 0: 586 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}agr_fisica
Agresión física últimos 12 meses
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| agr_fisica | Agresión física últimos 12 meses | factor | FALSE | 1. 0, 2. 1 |
7 | 0.9964912 | 2 | 0: 1968, 1: 20 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}acos_sex
Acoso sexual últimos 12 meses
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)7 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| acos_sex | Acoso sexual últimos 12 meses | factor | FALSE | 1. 0, 2. 1 |
7 | 0.9964912 | 2 | 0: 1961, 1: 27 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lf_rec_logit
Suma puntajes dicotomizados Lid Laissez-Faire Leymann (OCM), 3 niveles(ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lf_rec_logit | Suma puntajes dicotomizados Lid Laissez-Faire Leymann (OCM), 3 niveles(ags) | factor | FALSE | 1. 0, 2. 1, 3. 2 |
0 | 1 | 3 | 0: 1465, 1: 380, 2: 150 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}la_rec_logit
Suma puntajes dicotomizados Lid Autoritario Leymann (OCM), 3 niveles(ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| la_rec_logit | Suma puntajes dicotomizados Lid Autoritario Leymann (OCM), 3 niveles(ags) | factor | FALSE | 1. 0, 2. 1, 3. 2 |
0 | 1 | 3 | 0: 1691, 1: 213, 2: 91 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lc_rec_logit
Suma puntajes dicotomizados Lid Constructivo Leymann (OCM), 3 niveles(ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lc_rec_logit | Suma puntajes dicotomizados Lid Constructivo Leymann (OCM), 3 niveles(ags) | factor | FALSE | 1. 0, 2. 1, 3. 2 |
0 | 1 | 3 | 0: 1156, 2: 553, 1: 286 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_dic_logit
Suma puntajes dicotomizados NAQ Leymann (OCM), 3 niveles(ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_dic_logit | Suma puntajes dicotomizados NAQ Leymann (OCM), 3 niveles(ags) | factor | FALSE | 1. 0, 2. 1, 3. 2 |
0 | 1 | 3 | 0: 1573, 1: 224, 2: 198 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des_sum
Suma puntajes brutos (5, 7, 9, 1, 4, 8, 10) Lid Destructivo
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| lid_des_sum | Suma puntajes brutos (5, 7, 9, 1, 4, 8, 10) Lid Destructivo | numeric | 0 | 1 | 0 | 2 | 20 | 2.805013 | 3.094074 | ▇▂▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des_sum_cuartil
NAQ por cuartiles (Por defecto, con 2000 casos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des_sum_cuartil | NAQ por cuartiles (Por defecto, con 2000 casos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
0 | 1 | 4 | 0: 573, 1: 569, 3: 437, 2: 416 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_sum_cuartil
Suma ptjes brutos NAQ-R (por cuartiles) (correcto 1995 casos)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_sum_cuartil | Suma ptjes brutos NAQ-R (por cuartiles) (correcto 1995 casos) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3 |
0 | 1 | 4 | 1: 584, 0: 513, 3: 465, 2: 433 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_1
Pertenencia Esperada a la Clase 1 (>.5), Cultura por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_1 | Pertenencia Esperada a la Clase 1 (>.5), Cultura por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1588, 1: 407 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_2
Pertenencia Esperada a la Clase 2 (>.5), Cultura por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_2 | Pertenencia Esperada a la Clase 2 (>.5), Cultura por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1668, 1: 327 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_3
Pertenencia Esperada a la Clase 3 (>.5), Cultura por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_3 | Pertenencia Esperada a la Clase 3 (>.5), Cultura por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1733, 1: 262 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_4
Pertenencia Esperada a la Clase 4 (>.5), Cultura por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_4 | Pertenencia Esperada a la Clase 4 (>.5), Cultura por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1654, 1: 341 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_5
Pertenencia Esperada a la Clase 5 (>.5), Cultura por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_5 | Pertenencia Esperada a la Clase 5 (>.5), Cultura por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1775, 1: 220 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_6
Pertenencia Esperada a la Clase 6 (>.5), Cultura por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_6 | Pertenencia Esperada a la Clase 6 (>.5), Cultura por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1717, 1: 278 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_rec
Conjunto concatenado de clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| cluster_rec | Conjunto concatenado de clases (JPT) | character | 0 | 1 | 7 | 0 | 6 | 6 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_rec2
Recodificación Clases Latentes Cultura (1=.;2=6;3=5;4=4;5=3;6=2;7=1) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)160 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cluster_rec2 | Recodificación Clases Latentes Cultura (1=.;2=6;3=5;4=4;5=3;6=2;7=1) (JPT) | factor | FALSE | 1. clus 1, 2. clus 2, 3. clus 3, 4. clus 4, 5. clus 5, 6. clus 6 |
160 | 0.9197995 | 6 | clu: 407, clu: 341, clu: 327, clu: 278 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_alt_1
Pertenencia Esperada a la Clase 1 (>.5), Cultura Alternativa por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_alt_1 | Pertenencia Esperada a la Clase 1 (>.5), Cultura Alternativa por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1588, 1: 407 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_alt_2
Pertenencia Esperada a la Clase 2 (>.5), Cultura Alternativa por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_alt_2 | Pertenencia Esperada a la Clase 2 (>.5), Cultura Alternativa por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1733, 1: 262 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_alt_3
Pertenencia Esperada a la Clase 3 (>.5), Cultura Alternativa por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_alt_3 | Pertenencia Esperada a la Clase 3 (>.5), Cultura Alternativa por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1775, 1: 220 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_alt_4
Pertenencia Esperada a la Clase 4 (>.5), Cultura Alternativa por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_alt_4 | Pertenencia Esperada a la Clase 4 (>.5), Cultura Alternativa por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1660, 1: 335 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_alt_5
Pertenencia Esperada a la Clase 5 (>.5), Cultura Alternativa por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_alt_5 | Pertenencia Esperada a la Clase 5 (>.5), Cultura Alternativa por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1717, 1: 278 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_culorg_alt_6
Pertenencia Esperada a la Clase 6 (>.5), Cultura Alternativa por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_culorg_alt_6 | Pertenencia Esperada a la Clase 6 (>.5), Cultura Alternativa por Clases (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1669, 1: 326 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classprior1
Probabilidad A priori de Pertenecer a la Clase 1, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| classprior1 | Probabilidad A priori de Pertenecer a la Clase 1, CULTURA Definitiva por Clases (JPT) | character | 0 | 1 | 1 | 0 | 8 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classprior2
Probabilidad A priori de Pertenecer a la Clase 2, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| classprior2 | Probabilidad A priori de Pertenecer a la Clase 2, CULTURA Definitiva por Clases (JPT) | character | 0 | 1 | 1 | 0 | 8 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classprior3
Probabilidad A priori de Pertenecer a la Clase 3, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| classprior3 | Probabilidad A priori de Pertenecer a la Clase 3, CULTURA Definitiva por Clases (JPT) | character | 0 | 1 | 1 | 0 | 8 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classprior4
Probabilidad A priori de Pertenecer a la Clase 4, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| classprior4 | Probabilidad A priori de Pertenecer a la Clase 4, CULTURA Definitiva por Clases (JPT) | character | 0 | 1 | 1 | 0 | 8 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classprior5
Probabilidad A priori de Pertenecer a la Clase 5, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| classprior5 | Probabilidad A priori de Pertenecer a la Clase 5, CULTURA Definitiva por Clases (JPT) | character | 0 | 1 | 1 | 0 | 8 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classprior6
Probabilidad A priori de Pertenecer a la Clase 6, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| classprior6 | Probabilidad A priori de Pertenecer a la Clase 6, CULTURA Definitiva por Clases (JPT) | character | 0 | 1 | 1 | 0 | 8 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_alt_1
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 1 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1588 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_alt_1 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 1 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1588 | 0.20401 | 1 | 1: 407 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_alt_2
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 2 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1733 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_alt_2 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 2 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1733 | 0.1313283 | 1 | 1: 262 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_alt_3
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 3 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1775 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_alt_3 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 3 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1775 | 0.1102757 | 1 | 1: 220 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_alt_4
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 4 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1660 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_alt_4 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 4 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1660 | 0.1679198 | 1 | 1: 335 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_alt_5
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 5 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1717 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_alt_5 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 5 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1717 | 0.1393484 | 1 | 1: 278 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_alt_6
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 6 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1669 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_alt_6 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 6 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1669 | 0.1634085 | 1 | 1: 326 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_alt
Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| cluster_alt | Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT) | character | 0 | 1 | 7 | 0 | 6 | 6 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_rec_alt
Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| cluster_rec_alt | Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT) | character | 0 | 1 | 7 | 0 | 6 | 6 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_1
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 1 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1588 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_1 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 1 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1588 | 0.20401 | 1 | 1: 407 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_2
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 2 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1668 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_2 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 2 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1668 | 0.1639098 | 1 | 1: 327 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_3
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 3 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1733 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_3 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 3 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1733 | 0.1313283 | 1 | 1: 262 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_4
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 4 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1654 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_4 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 4 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1654 | 0.1709273 | 1 | 1: 341 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_5
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 5 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1775 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_5 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 5 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1775 | 0.1102757 | 1 | 1: 220 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_culorg_6
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 6 (6 clases) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1717 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_culorg_6 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 6 (6 clases) (JPT) | factor | FALSE | 1. 1 | 1717 | 0.1393484 | 1 | 1: 278 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_af_sum
Suma de puntajes brutos NAQ Acoso Físico (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| naq_r_af_sum | Suma de puntajes brutos NAQ Acoso Físico (ags) | numeric | 0 | 1 | 0 | 0 | 12 | 0.5954887 | 1.316617 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_ap_sum
Suma de puntajes brutos NAQ Acoso Psicológico (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| naq_r_ap_sum | Suma de puntajes brutos NAQ Acoso Psicológico (ags) | numeric | 0 | 1 | 0 | 1 | 48 | 3.509273 | 5.467721 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_r_rt_sum
Suma de puntajes brutos NAQ Acoso Relacionado con el Trabajo (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| naq_r_rt_sum | Suma de puntajes brutos NAQ Acoso Relacionado con el Trabajo (ags) | numeric | 0 | 1 | 0 | 2 | 28 | 3.042607 | 3.810939 | ▇▂▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}democ_paper
Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| democ_paper | Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21) (ags) | numeric | 0 | 1 | 0 | 20 | 36 | 20.19499 | 4.936566 | ▁▂▇▅▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lf2
Suma de puntajes brutos Liderazgo Laissez-Faire (solo variables validadas) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| lf2 | Suma de puntajes brutos Liderazgo Laissez-Faire (solo variables validadas) (ags) | numeric | 0 | 1 | 0 | 1 | 9 | 1.718296 | 1.790939 | ▇▅▂▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lc2
Suma de puntajes brutos Liderazgo Constructivo (solo variables validadas) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| lc2 | Suma de puntajes brutos Liderazgo Constructivo (solo variables validadas) (ags) | numeric | 0 | 1 | 0 | 6 | 12 | 5.909273 | 3.53295 | ▆▆▇▆▅ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des_dic
Suma puntajes dicotomizado (DLS) (Dichotomized sum score) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des_dic | Suma puntajes dicotomizado (DLS) (Dichotomized sum score) (ags) | factor | FALSE | 1. 0, 2. 1, 3. 2, 4. 3, 5. 4 o más |
0 | 1 | 5 | 0: 1350, 1: 377, 2: 141, 4 o: 68 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des_dic_logit
Suma puntajes dicotomizados DLS Leymann (OCM), 3 niveles(ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des_dic_logit | Suma puntajes dicotomizados DLS Leymann (OCM), 3 niveles(ags) | factor | FALSE | 1. 0, 2. 1, 3. 2 |
0 | 1 | 3 | 0: 1350, 1: 377, 2: 268 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}bajarecompensa
Baja Recompensa (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)136 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| bajarecompensa | Baja Recompensa (Art JPT) | factor | FALSE | 1. Alta recompensa, 2. Baj recompensa |
136 | 0.9318296 | 2 | Alt: 1098, Baj: 761 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}altoesfuerzo
Alto Esfuerzo (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)10 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| altoesfuerzo | Alto Esfuerzo (Art JPT) | factor | FALSE | 1. Alto esfuerzo, 2. Baj esfuerzo |
10 | 0.9949875 | 2 | Baj: 1163, Alt: 822 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}desbalance
esfuerzo/(recompensas* 0.4285)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)141 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| desbalance | esfuerzo/(recompensas* 0.4285) | numeric | 141 | 0.9293233 | 0.25 | 0.91 | 4 | 0.9695429 | 0.4154556 | ▇▅▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}desbalance_dic
Desbalance Esfuerzo-Recompensa
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)141 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| desbalance_dic | Desbalance Esfuerzo-Recompensa | factor | FALSE | 1. Balance Esf-Rec, 2. DER |
141 | 0.9293233 | 2 | Bal: 1111, DER: 743 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}culorg1_ext
CO Desbalanceada en Eje Resultados vs. Procesos (puntajes 3 y 4=0)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| culorg1_ext | CO Desbalanceada en Eje Resultados vs. Procesos (puntajes 3 y 4=0) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1158, 0: 837 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}culorg2_ext
CO Desbalanceada en Eje Control Estricto vs. Laxo (puntajes 3 y 4=0)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| culorg2_ext | CO Desbalanceada en Eje Control Estricto vs. Laxo (puntajes 3 y 4=0) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1071, 0: 924 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}culorg3_ext
CO Desbalanceada en Eje Tareas vs. Bienestar Trabajadores (puntajes 3 y 4=0)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| culorg3_ext | CO Desbalanceada en Eje Tareas vs. Bienestar Trabajadores (puntajes 3 y 4=0) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 1: 1117, 0: 878 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}proc_res
CO Balanceada en Eje Resultados vs. Procesos (1=3/4) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| proc_res | CO Balanceada en Eje Resultados vs. Procesos (1=3/4) (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1158, 1: 837 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}bienst_tarea
CO Balanceada en Eje Tareas vs. Bienestar Trabajadores (1=3/4) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| bienst_tarea | CO Balanceada en Eje Tareas vs. Bienestar Trabajadores (1=3/4) (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1071, 1: 924 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}laxo_estr
CO Balanceada en Eje Control Estricto vs. Laxo (1=3/4) (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| laxo_estr | CO Balanceada en Eje Control Estricto vs. Laxo (1=3/4) (JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1117, 1: 878 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des_my_e
Exposición a Liderazgos Destructivos (OCM Mikkelsen y Einarsen) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des_my_e | Exposición a Liderazgos Destructivos (OCM Mikkelsen y Einarsen) (Art JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1727, 1: 268 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_rec2_binary
Clase a Comparar con el Resto de las Clases
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)160 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cluster_rec2_binary | Clase a Comparar con el Resto de las Clases | factor | FALSE | 1. 0, 2. 1 |
160 | 0.9197995 | 2 | 0: 1508, 1: 327 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_culorg_alt1
Probabilidad Posterior de Pertencer a la Clase 1, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_culorg_alt1 | Probabilidad Posterior de Pertencer a la Clase 1, CULTURA Definitiva por Clases (JPT) | numeric | 0 | 1 | 2.2e-23 | 0.0051 | 0.98 | 0.2063715 | 0.343539 | ▇▁▁▁▂ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_culorg_alt2
Probabilidad Posterior de Pertencer a la Clase 2, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_culorg_alt2 | Probabilidad Posterior de Pertencer a la Clase 2, CULTURA Definitiva por Clases (JPT) | numeric | 0 | 1 | 2.2e-14 | 0.0016 | 0.95 | 0.1693163 | 0.3185067 | ▇▁▁▁▂ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_culorg_alt3
Probabilidad Posterior de Pertencer a la Clase 3, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_culorg_alt3 | Probabilidad Posterior de Pertencer a la Clase 3, CULTURA Definitiva por Clases (JPT) | numeric | 0 | 1 | 3e-04 | 0.037 | 1 | 0.1631187 | 0.284972 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_culorg_alt4
Probabilidad Posterior de Pertencer a la Clase 4, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_culorg_alt4 | Probabilidad Posterior de Pertencer a la Clase 4, CULTURA Definitiva por Clases (JPT) | numeric | 0 | 1 | 5.6e-05 | 0.008 | 1 | 0.1853961 | 0.327455 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_culorg_alt5
Probabilidad Posterior de Pertencer a la Clase 5, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_culorg_alt5 | Probabilidad Posterior de Pertencer a la Clase 5, CULTURA Definitiva por Clases (JPT) | numeric | 0 | 1 | 1.6e-12 | 0.0059 | 0.98 | 0.1303475 | 0.2779792 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_culorg_alt6
Probabilidad Posterior de Pertencer a la Clase 6, CULTURA Definitiva por Clases (JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_culorg_alt6 | Probabilidad Posterior de Pertencer a la Clase 6, CULTURA Definitiva por Clases (JPT) | numeric | 0 | 1 | 2.1e-18 | 0.0056 | 0.96 | 0.1454499 | 0.2936595 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_rec2_alt
Clasificación por Cluster, Ítemes de Cultura (6 clases)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)167 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cluster_rec2_alt | Clasificación por Cluster, Ítemes de Cultura (6 clases) | factor | FALSE | 1. clus 1, 2. clus 2, 3. clus 3, 4. clus 4, 5. clus 5, 6. clus 6 |
167 | 0.9162907 | 6 | clu: 407, clu: 335, clu: 326, clu: 278 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}naq_sum_roc3
Suma ptjes brutos NAQ-R ROC (crit. inclusivo) (Art Xi y Am) (tesis ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| naq_sum_roc3 | Suma ptjes brutos NAQ-R ROC (crit. inclusivo) (Art Xi y Am) (tesis ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1406, 1: 589 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}educacion277
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| educacion277 | factor | FALSE | 1. Sin Educacion o Basica, 2. Media o Tecnica Incomp, 3. Tecnica Comp o Univ Incomp, 4. Universitaria Comp o Postgr |
6 | 0.9969925 | 4 | Med: 1012, Tec: 507, Uni: 282, Sin: 188 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ocuprev_xi_am277
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| ocuprev_xi_am277 | factor | FALSE | 1. Jef Intermedias, 2. Prof y Tecnicos, 3. Emp Oficina y Vendedores, 4. Op, conductores y calif, 5. No calificados |
4 | 0.997995 | 5 | Op,: 657, Emp: 469, No : 372, Pro: 332 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ing_hog_xi_am277
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)539 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| ing_hog_xi_am277 | factor | FALSE | 1. Hasta 540.000 pesos, 2. Desde 540.001 hasta 913.000 pesos, 3. Desde 913.001 o mas |
539 | 0.7298246 | 3 | Has: 746, Des: 491, Des: 219 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vul_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1294 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| vul_hm | factor | FALSE | 1. Vulnerable Mujer, 2. Vulnerable Hombre |
1294 | 0.3513784 | 2 | Vul: 386, Vul: 315 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lf_leyman_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1465 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| lf_leyman_hm | factor | FALSE | 1. Exp a Lid LF Mujer, 2. Exp a Lid LF Hombre |
1465 | 0.2656642 | 2 | Exp: 269, Exp: 261 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}la_leyman_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1691 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| la_leyman_hm | factor | FALSE | 1. Exp a Lid Aut Mujer, 2. Exp a Lid Aut Hombre |
1691 | 0.152381 | 2 | Exp: 167, Exp: 137 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lc_leyman_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)553 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| lc_leyman_hm | factor | FALSE | 1. Exp a Lid Cons Mujer, 2. Exp a Lid Cons Hombre |
553 | 0.722807 | 2 | Exp: 721, Exp: 721 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}depre_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1779 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| depre_hm | factor | FALSE | 1. Exp a SintDep Mujer, 2. Exp a SintDep Hombre |
1779 | 0.1082707 | 2 | Exp: 149, Exp: 67 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}psicotrop_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1730 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| psicotrop_hm | factor | FALSE | 1. Exp a ConsPsic Mujer, 2. Exp a ConsPsic Hombre |
1730 | 0.1328321 | 2 | Exp: 179, Exp: 86 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}k2_elevado_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1749 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| k2_elevado_hm | factor | FALSE | 1. Exp a Distres Mujer, 2. Exp a Distres Hombre |
1749 | 0.1233083 | 2 | Exp: 166, Exp: 80 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}climasex_hm
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1796 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| climasex_hm | factor | FALSE | 1. Exp a ClimaSex Mujer, 2. Exp a ClimaSex Hombre |
1796 | 0.0997494 | 2 | Exp: 110, Exp: 89 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}jerarquia_acoso
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1806 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| jerarquia_acoso | factor | FALSE | 1. Superiores, 2. Companeros, 3. Subalternos, 4. Externos |
1806 | 0.0947368 | 4 | Sup: 98, Ext: 42, Com: 41, Sub: 8 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}jerarquia_acoso2
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1967 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts | label |
|---|---|---|---|---|---|---|---|---|
| jerarquia_acoso2 | factor | FALSE | 1. Companeros, 2. Subalternos, 3. Externos |
1967 | 0.0140351 | 3 | Com: 15, Ext: 9, Sub: 4 | NA |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lid_des_leyman
Exposición a Liderazgos Destructivos (OCM Leyman) (Art JPT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lid_des_leyman | Exposición a Liderazgos Destructivos (OCM Leyman) (Art JPT) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1350, 1: 645 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}acos_psico_freq
Acoso Psicológico Seguido y Muy Seguido (Art. Ximena y Amalia)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1796 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| acos_psico_freq | Acoso Psicológico Seguido y Muy Seguido (Art. Ximena y Amalia) | factor | FALSE | 1. 0, 2. 1 |
1796 | 0.0997494 | 2 | 0: 152, 1: 47 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}carga_dom
Y sin considerar su trabajo remunerado, durante la semana, ¿usted dedica además tiempo para hacer tareas domésticas y/o de cuidado, pero no remunerado? (A16)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)5 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| carga_dom | Y sin considerar su trabajo remunerado, durante la semana, ¿usted dedica además tiempo para hacer tareas domésticas y/o de cuidado, pero no remunerado? (A16) | factor | FALSE | 1. 0, 2. 1 |
5 | 0.9974937 | 2 | 1: 1524, 0: 466 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}educacion_media
Educación Menor a Nivel de Enseñanza Media Completa (Art. Magdalena)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)6 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| educacion_media | Educación Menor a Nivel de Enseñanza Media Completa (Art. Magdalena) | factor | FALSE | 1. 0, 2. 1 |
6 | 0.9969925 | 2 | 0: 1586, 1: 403 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cont_def
Contrato Definido (Art. Magdalena)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)33 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cont_def | Contrato Definido (Art. Magdalena) | factor | FALSE | 1. 0, 2. 1 |
33 | 0.9834586 | 2 | 0: 1372, 1: 590 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ocuprev_nocalif
Ocupación Sin Calificación (Operarios, Técnicos, Conductores y Trabajdores No-Calificados) (Art. Magdalena)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| ocuprev_nocalif | Ocupación Sin Calificación (Operarios, Técnicos, Conductores y Trabajdores No-Calificados) (Art. Magdalena) | factor | FALSE | 1. 0, 2. 1 |
4 | 0.997995 | 2 | 0: 1009, 1: 982 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}santiago
Zona, Gran Santiago
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| santiago | Zona, Gran Santiago | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1196, 1: 799 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}valpo
Zona, Gran Valparaiso
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| valpo | Zona, Gran Valparaiso | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1398, 1: 597 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}concep
Zona, Gran Concepción
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| concep | Zona, Gran Concepción | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1396, 1: 599 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_naq_def_1
Probabilidad Posterior de Pertencer a la Clase 1, NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_naq_def_1 | Probabilidad Posterior de Pertencer a la Clase 1, NAQ por Clases (ags) | numeric | 2 | 0.9989975 | 0 | 0.0047 | 1 | 0.1393717 | 0.2714083 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_naq_def_2
Probabilidad Posterior de Pertencer a la Clase 2, NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_naq_def_2 | Probabilidad Posterior de Pertencer a la Clase 2, NAQ por Clases (ags) | numeric | 2 | 0.9989975 | 0 | 0.0053 | 0.99 | 0.1530158 | 0.2857753 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_naq_def_3
Probabilidad Posterior de Pertencer a la Clase 3, NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_naq_def_3 | Probabilidad Posterior de Pertencer a la Clase 3, NAQ por Clases (ags) | numeric | 2 | 0.9989975 | 3.3e-18 | 1.6e-13 | 1 | 0.0411621 | 0.1878065 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_naq_def_4
Probabilidad Posterior de Pertencer a la Clase 4, NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_naq_def_4 | Probabilidad Posterior de Pertencer a la Clase 4, NAQ por Clases (ags) | numeric | 2 | 0.9989975 | 0 | 3e-04 | 1 | 0.0540203 | 0.1848993 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_naq_def_5
Probabilidad Posterior de Pertencer a la Clase 5, NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_naq_def_5 | Probabilidad Posterior de Pertencer a la Clase 5, NAQ por Clases (ags) | numeric | 2 | 0.9989975 | 1.9e-21 | 3.2e-05 | 1 | 0.1573397 | 0.3355434 | ▇▁▁▁▁ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}classpost_naq_def_6
Probabilidad Posterior de Pertencer a la Clase 6, NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)2 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.stata |
|---|---|---|---|---|---|---|---|---|---|---|---|
| classpost_naq_def_6 | Probabilidad Posterior de Pertencer a la Clase 6, NAQ por Clases (ags) | numeric | 2 | 0.9989975 | 0 | 0.15 | 1 | 0.4550906 | 0.4700671 | ▇▁▁▁▇ | %9.0g |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_naq_alt_1
Pertenencia Esperada a la Clase 1 (>.6), NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_naq_alt_1 | Pertenencia Esperada a la Clase 1 (>.6), NAQ por Clases (ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1784, 1: 211 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_naq_alt_2
Pertenencia Esperada a la Clase 2 (>.6), NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_naq_alt_2 | Pertenencia Esperada a la Clase 2 (>.6), NAQ por Clases (ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1751, 1: 244 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_naq_alt_3
Pertenencia Esperada a la Clase 3 (>.6), NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_naq_alt_3 | Pertenencia Esperada a la Clase 3 (>.6), NAQ por Clases (ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1917, 1: 78 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_naq_alt_4
Pertenencia Esperada a la Clase 4 (>.6), NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_naq_alt_4 | Pertenencia Esperada a la Clase 4 (>.6), NAQ por Clases (ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1916, 1: 79 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_naq_alt_5
Pertenencia Esperada a la Clase 5 (>.6), NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_naq_alt_5 | Pertenencia Esperada a la Clase 5 (>.6), NAQ por Clases (ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1698, 1: 297 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}expclass_naq_alt_6
Pertenencia Esperada a la Clase 6 (>.6), NAQ por Clases (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| expclass_naq_alt_6 | Pertenencia Esperada a la Clase 6 (>.6), NAQ por Clases (ags) | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1092, 1: 903 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_naq_alt_1
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 1 (6 clases) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1784 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_naq_alt_1 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 1 (6 clases) (ags) | factor | FALSE | 1. 1 | 1784 | 0.1057644 | 1 | 1: 211 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_naq_alt_2
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 2 (6 clases) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1751 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_naq_alt_2 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 2 (6 clases) (ags) | factor | FALSE | 1. 1 | 1751 | 0.1223058 | 1 | 1: 244 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_naq_alt_3
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 3 (6 clases) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1917 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_naq_alt_3 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 3 (6 clases) (ags) | factor | FALSE | 1. 1 | 1917 | 0.0390977 | 1 | 1: 78 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_naq_alt_4
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 4 (6 clases) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1916 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_naq_alt_4 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 4 (6 clases) (ags) | factor | FALSE | 1. 1 | 1916 | 0.039599 | 1 | 1: 79 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_naq_alt_5
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 5 (6 clases) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1698 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_naq_alt_5 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 5 (6 clases) (ags) | factor | FALSE | 1. 1 | 1698 | 0.1488722 | 1 | 1: 297 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rec_expclass_naq_alt_6
Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 6 (6 clases) (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1092 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rec_expclass_naq_alt_6 | Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 6 (6 clases) (ags) | factor | FALSE | 1. 1 | 1092 | 0.4526316 | 1 | 1: 903 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_naq_alt
Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) NAQ (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| cluster_naq_alt | Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) NAQ (ags) | character | 0 | 1 | 8 | 0 | 6 | 6 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_naq_alt_rec
Clasificación preliminar (contiene a los que no pertenecen) NAQ (ags)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| cluster_naq_alt_rec | Clasificación preliminar (contiene a los que no pertenecen) NAQ (ags) | character | 0 | 1 | 8 | 0 | 6 | 6 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}cluster_rec2_naq_alt
Clasificación por Cluster, Ítemes de NAQ (6 clases)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)195 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| cluster_rec2_naq_alt | Clasificación por Cluster, Ítemes de NAQ (6 clases) | factor | FALSE | 1. class 1, 2. class 2, 3. class 3, 4. class 4, 5. class 5, 6. class 6 |
195 | 0.9022556 | 6 | cla: 901, cla: 295, cla: 242, cla: 209 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}turnos
Sistema de turnos
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| turnos | Sistema de turnos | factor | FALSE | 1. 0, 2. 1 |
0 | 1 | 2 | 0: 1633, 1: 362 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}missingness_reportMissingness report
if (length(md_pattern)) {
if (knitr::is_html_output()) {
rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
} else {
knitr::kable(md_pattern)
}
}itemsCodebook table
export_table(metadata_table)jsonldJSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "FONDECYT_1170239 A Marzo 2020",
"description": "Prevalencia, formas de manifestacion y factores facilitadores de la violencia laboral, y sus efectos en la salud mental en poblacion asalariada en Chile atendiendo desiqualdades sociales y de genero. Inv. Responsable Elisa Ansoleaga\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.2).",
"creator": "Andres Gonzalez Santa Cruz",
"datePublished": "2020-03-24",
"keywords": ["sbj_num", "n_persona", "a2", "a3", "a4", "a5", "a6", "a7", "a8", "a9", "a10", "a11", "a12_o1", "a12_o2", "a12_o3", "a12_o4", "a12_o5", "a12_o6", "a12_otra_1", "a12_otra_2", "a13", "a14", "a14_1", "a15", "a15_1", "a16", "a16_1", "a17", "a18_top_1", "a18_top_2", "a18_top_3", "a18_top_4", "a18_top_5", "a18_top_6", "a18_top_7", "a18_top_8", "a18_top_9", "a18_top_10", "a19_top_1", "a19_top_2", "a19_top_3", "a19_top_4", "a20_1_top_1", "a20_1_top_2", "a20_1_top_3", "a20_1_top_4", "a20_1_top_5", "a20_1_top_6", "a20_1_top_7", "a20_1_top_8", "a20_1_top_9", "a20_1_top_10", "a20_1_top_11", "a20_1_top_12", "a20_2_top_13", "a20_2_top_14", "a20_2_top_15", "a20_2_top_16", "a20_2_top_17", "a20_2_top_18", "a20_2_top_19", "a20_2_top_20", "a20_2_top_21", "a20_2_top_22", "a20_2_top_23", "a20_2_top_24", "a21", "a21_1", "a23", "a25", "a27", "a28", "a29_01", "a29_02", "a29_1", "a22", "a24", "a26", "a30", "a31_top_1", "a31_top_2", "a31_top_3", "a31_top_4", "a31_top_5", "a31_top_6", "a31_top_7", "a31_top_8", "a31_top_9", "a31_top_10", "a31_top_11", "a31_top_12", "a31_top_13", "a31_top_14", "a31_top_15", "a31_top_16", "a32", "a33_coded_1", "a33_coded_2", "a33_coded_3", "a34_top_1", "a34_top_2", "a34_top_3", "a34_top_4", "a34_top_5", "a34_top_6", "a34_top_7", "a34_top_8", "a34_top_9", "a34_top_10", "a35_top_1", "a35_top_2", "a35_top_3", "a35_top_4", "a35_top_5", "a35_top_6", "a36", "a37_1", "a37_2", "a37_3", "a38_top_1", "a38_top_2", "a38_top_3", "a38_top_4", "a38_top_5", "a38_top_6", "a38_top_7", "a38_top_8", "a38_top_9", "a38_top_10", "a38_top_11", "a38_top_12", "a38_top_13", "a38_top_14", "a39_top_1", "a39_top_2", "a39_top_3", "a39_top_4", "a39_top_5", "a39_top_6", "a39_top_7", "a40", "a41", "a42", "a43_top_1", "a43_top_2", "a43_top_3", "a43_top_4", "a43_top_5", "a44", "a45_o1", "a45_o2", "a45_o3", "a45_o4", "a45_o5", "a46_top_1", "a46_top_12", "a46_top_13", "a47_top_1", "a47_top_2", "a47_top_3", "a47_top_4", "a47_top_5", "a47_top_6", "a47_top_7", "a48", "a49", "a50", "a51_1", "a51_2", "a51_3", "a52", "a53", "a54", "a56", "a55", "a57_1", "a63", "a58", "a59", "a60", "a61", "a62", "weight", "zona", "gse", "edux", "ocux", "edad", "tamano_de_la_empresa", "a1x1_coded_1", "a1x2_coded_1", "a11_recod", "a48a49", "depre", "estrechez", "mujer", "psicotrop", "frec_acos21_1", "frec_acos_ecotest", "testigo", "k2_muyelevado", "k2_elevado", "k6_2", "prec1", "prec2", "prec4", "prec5", "prec6", "antiguedad", "politrab", "desempleo", "trabdom", "atpub", "khab", "kcont", "latdec", "latdec_dic", "democ", "prec8", "prec9", "estabilidad", "prec7", "democ_dic", "vul1", "vul2", "vul3", "vul4", "vul5", "vul6", "vul7", "vul", "auditc", "prec3r", "aposoc1", "aposoc2", "aposoc3", "aposoc4", "aposoc5", "aposoc6", "aposoc_tot", "tenpsi", "iso", "salgen", "climasex", "culorg1", "culorg2", "culorg3", "cultura", "lid_des1", "lid_des2", "lid_des3", "lid_des4", "lid_des5", "lid_des6", "lid_des7", "lid_des8", "lid_des9", "lid_des10", "lid_des11", "lid_des12", "lid_des13", "lid_des14", "lid_des1_rec", "lid_des2_rec", "lid_des3_rec", "lid_des4_rec", "lid_des5_rec", "lid_des6_rec", "lid_des7_rec", "lid_des8_rec", "lid_des9_rec", "lid_des10_rec", "lid_des11_rec", "lid_des12_rec", "lid_des13_rec", "lid_des14_rec", "lf", "la", "lc", "naq1", "naq2", "naq3", "naq4", "naq5", "naq6", "naq7", "naq8", "naq9", "naq10", "naq11", "naq12", "naq13", "naq14", "naq15", "naq16", "naq17", "naq18", "naq19", "naq20", "naq21", "naq22", "naq1_rec", "naq2_rec", "naq3_rec", "naq4_rec", "naq5_rec", "naq6_rec", "naq7_rec", "naq8_rec", "naq9_rec", "naq10_rec", "naq11_rec", "naq12_rec", "naq13_rec", "naq14_rec", "naq15_rec", "naq16_rec", "naq17_rec", "naq18_rec", "naq19_rec", "naq20_rec", "naq21_rec", "naq22_rec", "naq_dic", "naq_leyman", "naq_my_e", "naq_a", "mas_horas", "menos45", "quieren_mas", "prec3", "prec", "precr", "precr1", "tamano", "r1", "r2", "r3", "r4", "r5", "r6", "r7", "e1", "e2", "e3", "esfuerzo", "recompensas", "la_rec", "lf_rec", "lc_rec", "lf_leyman", "la_leyman", "lc_leyman", "naq_sum", "k6", "s1", "s2", "s3", "s4", "s5", "satis", "ocuprev", "naq_r_af_rec", "naq_r_ap_rec", "naq_r_rt_rec", "naq_af_leyman", "naq_ap_leyman", "naq_rt_leyman", "naq_af_my_e", "naq_ap_my_e", "naq_rt_my_e", "naq_af_a", "naq_ap_a", "naq_rt_a", "satlab_rec", "araucaria_acoso", "acoso_auto", "acoso_auto_rec", "naq_sum_roc", "naq_sum_roc_3cat", "hombre", "autonaq", "naq23", "naq24", "naq23_rec", "naq24_rec", "naq_r_incivismo_rec", "naq_inciv_leyman", "prev3", "superv", "ocuprev_rec", "sect_priv", "cont_trab", "org_sind", "gse_ac3", "jef_hogar", "agr_fisica", "acos_sex", "lf_rec_logit", "la_rec_logit", "lc_rec_logit", "naq_dic_logit", "lid_des_sum", "lid_des_sum_cuartil", "naq_sum_cuartil", "expclass_culorg_1", "expclass_culorg_2", "expclass_culorg_3", "expclass_culorg_4", "expclass_culorg_5", "expclass_culorg_6", "cluster_rec", "cluster_rec2", "expclass_culorg_alt_1", "expclass_culorg_alt_2", "expclass_culorg_alt_3", "expclass_culorg_alt_4", "expclass_culorg_alt_5", "expclass_culorg_alt_6", "classprior1", "classprior2", "classprior3", "classprior4", "classprior5", "classprior6", "rec_expclass_culorg_alt_1", "rec_expclass_culorg_alt_2", "rec_expclass_culorg_alt_3", "rec_expclass_culorg_alt_4", "rec_expclass_culorg_alt_5", "rec_expclass_culorg_alt_6", "cluster_alt", "cluster_rec_alt", "rec_expclass_culorg_1", "rec_expclass_culorg_2", "rec_expclass_culorg_3", "rec_expclass_culorg_4", "rec_expclass_culorg_5", "rec_expclass_culorg_6", "naq_r_af_sum", "naq_r_ap_sum", "naq_r_rt_sum", "democ_paper", "lf2", "lc2", "lid_des_dic", "lid_des_dic_logit", "bajarecompensa", "altoesfuerzo", "desbalance", "desbalance_dic", "culorg1_ext", "culorg2_ext", "culorg3_ext", "proc_res", "bienst_tarea", "laxo_estr", "lid_des_my_e", "cluster_rec2_binary", "classpost_culorg_alt1", "classpost_culorg_alt2", "classpost_culorg_alt3", "classpost_culorg_alt4", "classpost_culorg_alt5", "classpost_culorg_alt6", "cluster_rec2_alt", "naq_sum_roc3", "educacion277", "ocuprev_xi_am277", "ing_hog_xi_am277", "vul_hm", "lf_leyman_hm", "la_leyman_hm", "lc_leyman_hm", "depre_hm", "psicotrop_hm", "k2_elevado_hm", "climasex_hm", "jerarquia_acoso", "jerarquia_acoso2", "lid_des_leyman", "acos_psico_freq", "carga_dom", "educacion_media", "cont_def", "ocuprev_nocalif", "santiago", "valpo", "concep", "classpost_naq_def_1", "classpost_naq_def_2", "classpost_naq_def_3", "classpost_naq_def_4", "classpost_naq_def_5", "classpost_naq_def_6", "expclass_naq_alt_1", "expclass_naq_alt_2", "expclass_naq_alt_3", "expclass_naq_alt_4", "expclass_naq_alt_5", "expclass_naq_alt_6", "rec_expclass_naq_alt_1", "rec_expclass_naq_alt_2", "rec_expclass_naq_alt_3", "rec_expclass_naq_alt_4", "rec_expclass_naq_alt_5", "rec_expclass_naq_alt_6", "cluster_naq_alt", "cluster_naq_alt_rec", "cluster_rec2_naq_alt", "turnos"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "sbj_num",
"description": "SbjNum",
"@type": "propertyValue"
},
{
"name": "n_persona",
"description": "A. ¿Cuántas personas de 20 o más años que trabajan en forma dependiente o asalariada, viven actualmente en esta vivienda?",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5",
"@type": "propertyValue"
},
{
"name": "a2",
"description": "En la empresa u organización donde usted trabaja ¿Supervisa a empleados/as?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a3",
"description": "¿A qué actividad económica se dedica la org. en la que trabaja?",
"value": "1. Agricultur,\n2. Alimentaci,\n3. Armada/Fue,\n4. Aseo,\n5. Asesor?a,\n6. Bienes inm,\n7. Carga/Puer,\n8. Carpinter?,\n9. Centro de,\n10. Comunicaci,\n11. Construcci,\n12. Cuidado de,\n13. Deporte,\n14. Distribuci,\n15. Electricid,\n16. Empresa de,\n17. Establecim,\n18. Eventos,\n19. Fotograf?a,\n20. Industria,\n21. Inform?tic,\n22. Ingenier?a,\n23. Jardiner?a,\n24. L?nea a?re,\n25. Mantenci?n,\n26. Minas y ca,\n27. Multitiend,\n28. nr,\n29. Otros,\n30. Otros come,\n31. Otros serv,\n32. Prestaci?n,\n33. Publicidad,\n34. Reparacion,\n35. Restaurant,\n36. Servicios,\n37. Transporte,\n38. Turismo",
"@type": "propertyValue"
},
{
"name": "a4",
"description": "¿Usted trabaja para un organismo público o para una empresa privada?",
"value": "1. De las Fue,\n2. No sabe,\n3. nr,\n4. Organismo,\n5. Otros,\n6. Sector pri",
"@type": "propertyValue"
},
{
"name": "a5",
"description": "En este trabajo ¿Tiene contrato o acuerdo de trabajo escrito?",
"value": "1. No,\n2. No sabe,\n3. nr,\n4. S?",
"@type": "propertyValue"
},
{
"name": "a6",
"description": "¿Qué tipo de contrato o acuerdo de trabajo tiene?",
"value": "1. A contrata,\n2. Con plazo,\n3. Contrato a,\n4. De deposit,\n5. De palabra,\n6. Indefinido,\n7. No sabe,\n8. No tiene c,\n9. nr,\n10. Otro,\n11. Pago por d",
"@type": "propertyValue"
},
{
"name": "a7",
"description": "Incluyéndose Ud ¿cuántas personas trabajan en su lugar de trabajo?",
"value": "1. 200 y m?s,\n2. De 5 a 10,\n3. Entre 11 y,\n4. Entre 50 y,\n5. Menos de 5,\n6. No sabe,\n7. nr",
"@type": "propertyValue"
},
{
"name": "a8",
"description": "Habitualmente en su trabajo principal, ¿cuántas hrs trabaja en la semana?",
"value": "1. Jornada co,\n2. Jornada ex,\n3. Jornada pa,\n4. No sabe (N,\n5. NR (NO LEE",
"@type": "propertyValue"
},
{
"name": "a9",
"description": "¿Usted desearía trabajar?",
"value": "1. Est? satis,\n2. M?s horas,\n3. Menos hora,\n4. NR (NO LEE",
"@type": "propertyValue"
},
{
"name": "a10",
"description": "Respecto a su horario de trabajo, ¿este es?",
"value": "1. En turnos,\n2. Fines de s,\n3. Horario li,\n4. Segun es n,\n5. Solo diurn,\n6. Solo noctu,\n7. Variable",
"@type": "propertyValue"
},
{
"name": "a11",
"description": "Podría decir si la remuneración de su trabajo principal, es:",
"value": "1. Es variabl,\n2. Fija,\n3. No sabe (N,\n4. Tiene una",
"@type": "propertyValue"
},
{
"name": "a12_o1",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": "1. Depende de,\n2. nr",
"@type": "propertyValue"
},
{
"name": "a12_o2",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": "1. Depende de",
"@type": "propertyValue"
},
{
"name": "a12_o3",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": "1. Depende de",
"@type": "propertyValue"
},
{
"name": "a12_o4",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": ". ",
"@type": "propertyValue"
},
{
"name": "a12_o5",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": ". ",
"@type": "propertyValue"
},
{
"name": "a12_o6",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": ". ",
"@type": "propertyValue"
},
{
"name": "a12_otra_1",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": "1. Depende de,\n2. Horas extr,\n3. Imposicion,\n4. Otra",
"@type": "propertyValue"
},
{
"name": "a12_otra_2",
"description": "Puede decirnos ¿de qué depende la parte variable de sus remuneraciones?",
"value": "1. Depende de",
"@type": "propertyValue"
},
{
"name": "a13",
"description": "¿Puede decirnos cuánto tiempo lleva trabajando en esta empresa u organización?",
"value": "1. Entre 6 me,\n2. M?s de 3 a,\n3. Menos de 6,\n4. NR (NO LEE",
"@type": "propertyValue"
},
{
"name": "a14",
"description": "Además del trabajo principal, ¿usted destina tiempo a otro trabajo pagado?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a14_1",
"description": "¿Cuántas horas le dedica a la semana en total a este otro trabajo pagado?",
"@type": "propertyValue"
},
{
"name": "a15",
"description": "En los últimos 12 meses ¿usted ha estado desempleado?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a15_1",
"description": "En los últimos 12 meses, ¿Por cuánto tiempo ha estado desempleado?",
"@type": "propertyValue"
},
{
"name": "a16",
"description": "Sin considerar trabajo remunerado (semana), ¿dedica tiempo a tareas domésticas no remuneradas?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a16_1",
"description": "Y aproximadamente, ¿Cuántas horas a la semana le dedica al trabajo doméstico y/o de cuidado en total?",
"@type": "propertyValue"
},
{
"name": "a17",
"description": "En su trabajo, ¿tiene habitualmente contacto directo con público, de viva voz o por teléfono (pacientes, clientes, alumnos, apoderados)?",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a18_top_1",
"description": "Mi trabajo exige que yo aprenda cosas nuevas (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_2",
"description": "Mi trabajo exige un alto nivel de calificaciones o habilidades (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_3",
"description": "Mi trabajo consiste en hacer siempre las mismas cosas (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_4",
"description": "Tengo libertad de decidir cómo hacer mi trabajo (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_5",
"description": "Tengo suficiente influencia en cómo ocurren las cosas en mi trabajo (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_6",
"description": "Mi trabajo exige trabajar muy rápido (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_7",
"description": "Me piden hacer una cantidad excesiva de trabajo (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_8",
"description": "Tengo suficiente tiempo para hacer mi trabajo (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_9",
"description": "Recibo demandas contradictorias por parte de otras personas (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a18_top_10",
"description": "Mi trabajo me exige un trabajo mental muy intenso (JCQ)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des",
"@type": "propertyValue"
},
{
"name": "a19_top_1",
"description": "¿Hay en su trabajo momentos y/o situaciones que le producen desgaste emocional? (DEMOC)",
"value": "1. Algunas ve,\n2. Muchas vec,\n3. Nunca/casi,\n4. Siempre",
"@type": "propertyValue"
},
{
"name": "a19_top_2",
"description": "En general, ¿considera usted que su trabajo le produce desgaste emocional? (DEMOC)",
"value": "1. Algunas ve,\n2. Muchas vec,\n3. Nunca/casi,\n4. Siempre",
"@type": "propertyValue"
},
{
"name": "a19_top_3",
"description": "En su trabajo, ¿tiene usted que guardar sus emociones y no expresarlas? (DEMOC)",
"value": "1. Algunas ve,\n2. Muchas vec,\n3. Nunca/casi,\n4. Siempre",
"@type": "propertyValue"
},
{
"name": "a19_top_4",
"description": "En su trabajo, ¿tiene usted que guardar sus opiniones y no expresarlas? (DEMOC)",
"value": "1. Algunas ve,\n2. Muchas vec,\n3. Nunca/casi,\n4. Siempre",
"@type": "propertyValue"
},
{
"name": "a20_1_top_1",
"description": "Alguien le ha ocultado información que ha afectado a su rendimiento.(NAQ1)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_2",
"description": "Ha sido humillado o ridiculizado en relación a su trabajo.(NAQ2)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_3",
"description": "Le han ordenado realizar un trabajo que está por debajo de su nivel de competencia o calificación.(NAQ3)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_4",
"description": "Le han cambiado de realizar tareas de responsabilidad por otras más triviales o desagradables.(NAQ4)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_5",
"description": "Se han extendido rumores sobre usted.(NAQ5)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_6",
"description": "Ha sido ignorado, excluido o le han dejado de hablar.(NAQ6)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_7",
"description": "Le han insultado u ofendido con comentarios sobre usted, sus actitudes o su vida privada.(NAQ7)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_8",
"description": "Le han gritado o ha sido objeto de enfados espontáneos.(NAQ8)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_9",
"description": "Ha sufrido conductas intimidatorias como ser apuntado con el dedo, la invasión de su espacio personal, empujones, que no le dejen pasar, etc.(NAQ9)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_10",
"description": "Ha visto detalles o indirectas de otros que le sugieran abandonar su trabajo.(NAQ10)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_11",
"description": "Le han recordado continuamente sus errores y fallos.(NAQ11)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_1_top_12",
"description": "Ha sido ignorado o ha recibido una reacción hostil cuando se ha acercado a alguien.(NAQ12)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_13",
"description": "Ha recibido críticas persistentes sobre su trabajo y esfuerzo(NAQ13)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_14",
"description": "Sus opiniones y puntos de vista han sido ignorados.(NAQ14)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_15",
"description": "Ha recibido bromas pesadas de gente con la que no se lleva bien.(NAQ15)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_16",
"description": "Le han asignado tareas u objetivos inalcanzables.(NAQ16)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_17",
"description": "Ha recibido acusaciones (reclamos) en su contra.(NAQ17)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_18",
"description": "Ha sido excesivamente supervisado en su trabajo.(NAQ18)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_19",
"description": "Ha sido presionado para no reclamar algo a lo que tiene derecho (p. Ej., licencia por enfermedad, vacaciones, viáticos, etc.)(NAQ19)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_20",
"description": "Ha sido objeto de numerosas tomaduras de pelo y sarcasmos.(NAQ20)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_21",
"description": "Ha sido expuesto a una excesiva carga de trabajo.(NAQ21)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_22",
"description": "Ha recibido amenazas de violencia o de abusos físicos.(NAQ22)",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_23",
"description": "Han dudado de su juicio sobre un asunto en el que tiene responsabilidad.",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a20_2_top_24",
"description": "Ha sido ignorado o excluido por el resto de sus compañeros o grupo profesional.",
"value": "1. A diario,\n2. Al menos 1,\n3. Alguna vez,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a21",
"description": "Si definimos el acoso como una situación en la que una persona percibe que continuamente es objeto de conductas negativas frente a las cuales tiene dificultades para defenderse ¿según esta definición, usted ha sido acosado/a durante los últimos 6 mese",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a21_1",
"description": "Y, ¿qué tan frecuentemente ha ocurrido ese acoso?",
"value": "1. De vez en,\n2. Pocas vece,\n3. Pr?cticame,\n4. Varias vec",
"@type": "propertyValue"
},
{
"name": "a23",
"description": "¿Con qué frecuencia le ha ocurrido esto?",
"value": "1. A veces,\n2. Muy seguid,\n3. Seguido",
"@type": "propertyValue"
},
{
"name": "a25",
"description": "¿Con qué frecuencia le ha ocurrido esto?",
"value": "1. A veces,\n2. NR (NO LEE,\n3. Seguido",
"@type": "propertyValue"
},
{
"name": "a27",
"description": "¿Con qué frecuencia le ha ocurrido esto?",
"value": "1. A veces,\n2. Muy seguid,\n3. NR (NO LEE,\n4. Seguido",
"@type": "propertyValue"
},
{
"name": "a28",
"description": "¿En los últimos 12 meses, en su trabajo principal ¿usted ha sido testigo de conductas, palabras o gestos de carácter sexual no deseado?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a29_01",
"description": "Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores?",
"value": "1. NR (NO LEE,\n2. Personas e,\n3. Sus compa?,\n4. Sus subalt,\n5. Sus superi",
"@type": "propertyValue"
},
{
"name": "a29_02",
"description": "Si usted ha sido objeto de acoso psicológico, agresión física o palabras o gestos de carácter sexual no deseados ¿Quiénes han sido principalmente los agresores?",
"value": "1. Personas e,\n2. Sus compa?,\n3. Sus subalt",
"@type": "propertyValue"
},
{
"name": "a29_1",
"description": "¿De qué sexo han sido los agresores?",
"value": "1. De ambos s,\n2. Mayoritari,\n3. nr,\n4. Solo hombr,\n5. Solo mujer",
"@type": "propertyValue"
},
{
"name": "a22",
"description": "Ahora, si consideramos los últimos 12 meses, en su trabajo principal, ¿ha sido alguna vez objeto de acoso psicológico, es decir de palabras o actos repetidos que atacan su dignidad o integridad?",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a24",
"description": "¿En su trabajo principal, en los últimos 12 meses, ha sido víctima de agresión física?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a26",
"description": "En los últimos 12 meses, en su trabajo principal ¿ha sido objeto de palabras o gestos de carácter sexual no deseado?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a30",
"description": "En caso de que usted haya sido objeto de alguna de las conductas señaladas, como acoso psicológico, agresión física o palabras o gestos de carácter sexual, ¿ha hecho algo para enfrentarlas?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_1",
"description": "Evitar encontrarse con los/as agresores/as",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_2",
"description": "Hacer uso de licencias médicas",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_3",
"description": "Evitar pensar en los hechos",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_4",
"description": "Volcarse en otras actividades (deporte, salidas, recreación en general)",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_5",
"description": "Renunciar a su trabajo",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_6",
"description": "Buscar apoyo en las creencias religiosas o espirituales.",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_7",
"description": "Realizar planes para mejorar la situación y llevarlos a cabo.",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_8",
"description": "Buscar información para solucionar la situación.",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_9",
"description": "Comentar lo sucedido con sus compañeros/as de trabajo.",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_10",
"description": "Comentar lo sucedido con familiares y/o amigos/as",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_11",
"description": "Buscar ayuda en personas que sufren el mismo problema.",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_12",
"description": "Hablar con el/los involucrado/s",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_13",
"description": "Hablar con superiores.",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_14",
"description": "Denunciar los hechos al sindicato",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_15",
"description": "Tomar acciones legales",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a31_top_16",
"description": "Otras acciones",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a32",
"description": "Y ¿usted ha vivido alguna situación en la que hayan sido vulnerados o pasados a llevar sus derechos en el trabajo?",
"value": "1. No,\n2. No sabe,\n3. nr,\n4. S?",
"@type": "propertyValue"
},
{
"name": "a33_coded_1",
"description": "¿Qué situación?, descríbala por favor.",
"value": "1. Abusos/ ac,\n2. Agresi?n v,\n3. Bajo pago,\n4. Clientes p,\n5. Cotizacion,\n6. Discrimina,\n7. En la part,\n8. Estafa a l,\n9. Exigencia,\n10. Extenden e,\n11. Inculpacio,\n12. Me despidi,\n13. No cumplen,\n14. No le dier,\n15. No pagan a,\n16. No respeta,\n17. No toman e,\n18. No valoran,\n19. NS/NC,\n20. Otros,\n21. Problemas,\n22. Tratan mal",
"@type": "propertyValue"
},
{
"name": "a33_coded_2",
"description": "¿Qué situación?, descríbala por favor.",
"value": "1. Abusos/ ac,\n2. Agresi?n v,\n3. Clientes p,\n4. Cotizacion,\n5. Exigencia,\n6. Extenden e,\n7. Inculpacio,\n8. Me despidi,\n9. No cumplen,\n10. No le dier,\n11. No pagan a,\n12. No respeta,\n13. No toman e,\n14. Otros,\n15. Problemas,\n16. Tratan mal",
"@type": "propertyValue"
},
{
"name": "a33_coded_3",
"description": "¿Qué situación?, descríbala por favor.",
"value": "1. Extenden e,\n2. Me despidi,\n3. No valoran,\n4. Problemas",
"@type": "propertyValue"
},
{
"name": "a34_top_1",
"description": "A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado (e1)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_2",
"description": "Me interrumpen o molestan con frecuencia en mi trabajo (e2)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_3",
"description": "En el último tiempo tengo cada vez más trabajo (e3)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_4",
"description": "Mis superiores me dan el reconocimiento que merezco (r1)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_5",
"description": "Las oportunidades de promoción en mi trabajo son escasas (r2)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_6",
"description": "Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.) (r3)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_7",
"description": "Mi estabilidad laboral es baja (r4)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_8",
"description": "Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado (r5)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_9",
"description": "Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas (r6)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a34_top_10",
"description": "Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado (r7)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a35_top_1",
"description": "Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a35_top_2",
"description": "Mi jefe inmediato consigue que la gente trabaje junta",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a35_top_3",
"description": "Mi jefe inmediato facilita la realización del trabajo",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a35_top_4",
"description": "Los(as) colegas con quienes trabajo se interesan o preocupan por mí",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a35_top_5",
"description": "Mis colegas facilitan la realización de mi trabajo",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a35_top_6",
"description": "En mi trabajo tengo la impresión de formar parte de un equipo",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a36",
"description": "¿En su lugar de trabajo hay un clima sexista?",
"value": "1. No,\n2. No sabe,\n3. nr,\n4. S?",
"@type": "propertyValue"
},
{
"name": "a37_1",
"description": "En mi empresa, (institución, organización) lo importante es cómo se hacen las cosas, no importan tanto los resultados.^^^En mi empresa, (institución, organización) lo importante son los resultados, no importa tanto cómo se hacen las cosas.",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6",
"@type": "propertyValue"
},
{
"name": "a37_2",
"description": "En mi empresa, (institución, organización) se preocupan del bienestar de los trabajadores más que del cumplimiento de las tareas.^^^En mi empresa, (institución, organización) se preocupan del cumplimiento de las tareas más que del bienestar de los trabajad",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6",
"@type": "propertyValue"
},
{
"name": "a37_3",
"description": "En mi empresa, (institución, organización) hay poca planificación y control, a veces hay que improvisar^^^En mi empresa, (institución, organización) hay una disciplina de trabajo muy estricta, todo está controlado y resulta predecible",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4,\n5. 5,\n6. 6",
"@type": "propertyValue"
},
{
"name": "a38_top_1",
"description": "¿Lo ha humillado a usted o a otros empleados/as, si no alcanzan los estándares esperados?(LID_DES1)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_2",
"description": "¿Ha incentivado las ideas o propuestas creativas o innovadoras?(LID_DES2)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_3",
"description": "¿Ha mostrado preocupación por los resultados?(LID_DES3)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_4",
"description": "¿Ha hecho gestos (por ejemplo, muecas, miradas, ademanes) a usted o a otros empleados para demostrar que no está satisfecho con su esfuerzo o el de los demás?(LID_DES4)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_5",
"description": "¿Ha evitado tomar decisiones?(LID_DES5)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_6",
"description": "El supervisor/la supervisora ¿reconoce el buen desempeño?(LID_DES6)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_7",
"description": "¿Ha evitado involucrarse en su trabajo?(LID_DES7)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_8",
"description": "¿Ha difundido información incorrecta sobre usted o sus compañeros/as de trabajo, con el fin de dañar su posición o la de ellos en la empresa?(LID_DES8)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_9",
"description": "¿Ha estado ausente cuando se le/a necesita?(LID_DES9)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_10",
"description": "¿Le ha retado por teléfono, le colgó (cortó), o enviado un correo grosero porque pensó que había hecho un mal trabajo?(LID_DES10)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_11",
"description": "¿Busca la manera de desarrollar sus capacidades?(LID_DES11)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_12",
"description": "¿Establece objetivos claros e inequívocos?(LID_DES12)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_13",
"description": "¿Define y explica claramente las tareas que le asigna a usted y a sus compañeros de trabajo?(LID_DES13)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a38_top_14",
"description": "¿Es flexible y está dispuesto/a a pensar de manera innovadora?(LID_DES14)",
"value": "1. Algunas ve,\n2. Bastante a,\n3. Casi siemp,\n4. nr,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a39_top_1",
"description": "Tiene miedo de reclamar mejores condiciones de trabajo",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a39_top_2",
"description": "Se siente indefenso/a ante el trato injusto de sus superiores",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a39_top_3",
"description": "Se siente preocupado de que le cambien las condiciones de su salario",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a39_top_4",
"description": "Tiene miedo de que lo despidan si no hace lo que le piden",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a39_top_5",
"description": "Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a39_top_6",
"description": "Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a39_top_7",
"description": "Le hacen sentir que usted puede ser fácilmente reemplazado (a)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a40",
"description": "¿En su empresa o institución existe alguna organización sindical o gremial?",
"value": "1. No,\n2. No sabe,\n3. nr,\n4. S?",
"@type": "propertyValue"
},
{
"name": "a41",
"description": "Pertenece usted a alguna organización sindical o gremial",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a42",
"description": "¿Tiene usted algún cargo de representación sindical?",
"value": "1. No,\n2. S?",
"@type": "propertyValue"
},
{
"name": "a43_top_1",
"description": "Me siento bastante satisfecho con mi trabajo actual (s1)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a43_top_2",
"description": "Casi todos los días estoy entusiasmado con mi trabajo (s2)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a43_top_3",
"description": "Cada día en el trabajo parece que nunca terminará (s3)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a43_top_4",
"description": "Disfruto con mi trabajo (s4)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a43_top_5",
"description": "Considero que mi trabajo es bastante desagradable (s5)",
"value": "1. De acuerdo,\n2. En desacue,\n3. Muy de acu,\n4. Muy en des,\n5. nr",
"@type": "propertyValue"
},
{
"name": "a44",
"description": "En general, diría usted que su salud es…",
"value": "1. Buena,\n2. Excelente,\n3. Mala,\n4. Muy buena,\n5. Muy mala,\n6. NR (NO LEE",
"@type": "propertyValue"
},
{
"name": "a45_o1",
"description": "En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?",
"value": "1. Accidente,\n2. Muerte de,\n3. Ninguna (N,\n4. NR (NO LEE,\n5. Ser v?ctim",
"@type": "propertyValue"
},
{
"name": "a45_o2",
"description": "En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?",
"value": "1. Accidente,\n2. Ser v?ctim",
"@type": "propertyValue"
},
{
"name": "a45_o3",
"description": "En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?",
"value": "1. Ser v?ctim",
"@type": "propertyValue"
},
{
"name": "a45_o4",
"description": "En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?",
"value": ". ",
"@type": "propertyValue"
},
{
"name": "a45_o5",
"description": "En los últimos seis meses ¿ha enfrentado algunas de las siguientes situaciones?",
"value": ". ",
"@type": "propertyValue"
},
{
"name": "a46_top_1",
"description": "¿Disminuir la ansiedad o el nerviosismo, tales como ansiolíticos? (CON_PSI)",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a46_top_12",
"description": "¿Ayudar a dormir, tales como hipnóticos? (CON_PSI)",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a46_top_13",
"description": "¿Remontar el ánimo tales como antidepresivos? (CON_PSI)",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a47_top_1",
"description": "Nervioso/a (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a47_top_2",
"description": "Desesperado/a (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a47_top_3",
"description": "Descontrolado/a o fuera de sí (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a47_top_4",
"description": "Tan deprimido/a que nada le puede hacer sonreír (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a47_top_5",
"description": "Que todo es un esfuerzo (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a47_top_6",
"description": "Bueno/a para nada (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a47_top_7",
"description": "Sin esperanza (K6)",
"value": "1. Algunas ve,\n2. La mayor?a,\n3. Nunca,\n4. Siempre,\n5. Solo unas",
"@type": "propertyValue"
},
{
"name": "a48",
"description": "Durante los últimos 12 meses, ¿se sintió triste o deprimido/a por dos semanas consecutivas o más?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a49",
"description": "Durante los últimos 12 meses, ¿llegó a sentir la pérdida de interés por la mayoría de las cosas que le gustaba hacer, tales como su trabajo, pasatiempos o cualquier actividad, por dos semanas seguidas o más?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a50",
"description": "Si usted ha sentido alguno de los problemas de salud antes señalados, como sentirse nervioso/a o con miedo, triste, deprimido/a, o ha consumido algún medicamento ¿a qué lo/s atribuye?",
"value": "1. A otra cir,\n2. Al trabajo,\n3. nr,\n4. S?lo al tr",
"@type": "propertyValue"
},
{
"name": "a51_1",
"description": "A continuación, quisiera hacerle algunas preguntas respecto a consumo de alcohol. ¿Qué tan seguido toma usted alguna bebida alcohólica?",
"value": "1. 1 vez al m,\n2. 2 a 3 vece,\n3. 2 a 4 vece,\n4. 4 o m?s ve,\n5. Nunca",
"@type": "propertyValue"
},
{
"name": "a51_2",
"description": "¿Cuántos tragos suele tomar usted en un día típico de consumo de alcohol?",
"value": "1. 0 a 2 trag,\n2. 10 o m?s t,\n3. 3 a 4 trag,\n4. 5 a 6 trag,\n5. 7 a 9 trag",
"@type": "propertyValue"
},
{
"name": "a51_3",
"description": "¿Qué tan seguido toma usted 5 o más tragos en una sola ocasión?",
"value": "1. Menos de u,\n2. Mensualmen,\n3. Nunca,\n4. Semanalmen,\n5. Todos o ca",
"@type": "propertyValue"
},
{
"name": "a52",
"description": "Sexo",
"value": "1. Hombre,\n2. Mujer",
"@type": "propertyValue"
},
{
"name": "a53",
"description": "Edad (años cumplidos)",
"@type": "propertyValue"
},
{
"name": "a54",
"description": "Indíqueme por favor, ¿cuál es su nivel educacional?",
"value": "1. Educaci?n,\n2. Estudios d,\n3. No ha asis,\n4. nr,\n5. T?cnica co,\n6. T?cnica in,\n7. Universida",
"@type": "propertyValue"
},
{
"name": "a56",
"description": "Por favor, indíqueme el nivel educacional del jefe de hogar",
"value": "1. Educaci?n,\n2. Estudios d,\n3. No ha asis,\n4. nr,\n5. T?cnica co,\n6. T?cnica in,\n7. Universida",
"@type": "propertyValue"
},
{
"name": "a55",
"description": "¿Es usted el/la jefe/a de hogar?",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "a57_1",
"description": "Jefe de Hogar:",
"value": "1. Alto ejecu,\n2. Ejecutivo,\n3. Empleado a,\n4. Estudiante,\n5. Jubilado,\n6. nr,\n7. Obrero cal,\n8. Oficio men,\n9. Sin empleo,\n10. Trabajos m",
"@type": "propertyValue"
},
{
"name": "a63",
"description": "TRABAJO DEL ENTREVISTADO:",
"value": "1. Alto ejecu,\n2. Ejecutivo,\n3. Empleado a,\n4. nr,\n5. Obrero cal,\n6. Oficio men,\n7. Trabajos m",
"@type": "propertyValue"
},
{
"name": "a58",
"description": "¿Nos podría decir en cuál de los siguientes tramos se encuentra su ingreso personal mensual? (considerando todos sus ingresos)",
"value": "1. De $1.000.,\n2. De $150.00,\n3. De $350.00,\n4. De $550.00,\n5. De $800.00,\n6. M?s de 1.5,\n7. Menos de $,\n8. nr",
"@type": "propertyValue"
},
{
"name": "a59",
"description": "Situación de pareja actual",
"value": "1. nr,\n2. Vive con p,\n3. Vive sin p",
"@type": "propertyValue"
},
{
"name": "a60",
"description": "¿Cuántas personas dependen económicamente de usted (totalmente o en parte)?",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4,\n6. 5,\n7. 6,\n8. 7,\n9. 8,\n10. 9,\n11. 99",
"@type": "propertyValue"
},
{
"name": "a61",
"description": "Por favor, indique la frase que más se acerca a la realidad de su hogar: “Pensando en el total de ingresos mensuales de su hogar, este…",
"value": "1. Les alcanz,\n2. No les alc,\n3. NR (NO LEE",
"@type": "propertyValue"
},
{
"name": "a62",
"description": "¿Me podría decir ¿en cuál de los siguientes tramos se encuentra aproximadamente el ingreso mensual total de su hogar?",
"value": "1. De $200.00,\n2. De $360.00,\n3. De $540.00,\n4. De $913.00,\n5. M?s de $1.,\n6. Menos de $,\n7. No sabe (N,\n8. NR (NO LEE",
"@type": "propertyValue"
},
{
"name": "weight",
"description": "Weight (ponderador)",
"@type": "propertyValue"
},
{
"name": "zona",
"description": "Región (Adimark)",
"value": "1. Gran Conce,\n2. Gran Santi,\n3. Gran Valpa",
"@type": "propertyValue"
},
{
"name": "gse",
"description": "GSE Encuestado/a (Adimark)",
"value": "1. ABC1,\n2. C2,\n3. C3,\n4. d,\n5. e",
"@type": "propertyValue"
},
{
"name": "edux",
"description": "Escolaridad (Adimark)",
"value": "1. Educaci?n,\n2. Estudios d,\n3. No ha asis,\n4. NR,\n5. T?cnica co,\n6. Universida",
"@type": "propertyValue"
},
{
"name": "ocux",
"description": "Situación Ocupacional (Adimark)",
"value": "1. Alto ejecu,\n2. Ejecutivo,\n3. Empleado a,\n4. nr,\n5. Obrero cal,\n6. Oficio men,\n7. Trabajos m",
"@type": "propertyValue"
},
{
"name": "edad",
"description": "Edad (en grupos) (Adimark)",
"value": "1. 20 a 29,\n2. 30 a 39,\n3. 40 a 49,\n4. 50 a 59,\n5. 60 a?os y",
"@type": "propertyValue"
},
{
"name": "tamano_de_la_empresa",
"description": "Tamaño de la Empresa (Adimark)",
"value": "1. 1 a 10,\n2. 11 a 49,\n3. 200 o m?s,\n4. 50 a 199",
"@type": "propertyValue"
},
{
"name": "a1x1_coded_1",
"description": "Ocupación (Adimark)",
"value": "1. Conductore,\n2. Empleados/,\n3. Jefaturas,\n4. nr,\n5. Operarios/,\n6. Profesiona,\n7. T?cnicos/a,\n8. Trabajador,\n9. Vendedores",
"@type": "propertyValue"
},
{
"name": "a1x2_coded_1",
"description": "Cargo (Adimark)",
"value": "1. Administra,\n2. Analista,\n3. Aseo/Auxil,\n4. Asistente,\n5. Atenci?n a,\n6. Auxiliar,\n7. Ayudante,\n8. Bombero,\n9. Cajera,\n10. Capataz,\n11. Cargadora,\n12. Chofer/con,\n13. Cocinero/,\n14. Conserje,\n15. Construcci,\n16. Contador,\n17. Contratist,\n18. Control de,\n19. Coordinado,\n20. Corredora,\n21. Costurera,\n22. Cuidador,\n23. Digitador,\n24. Director,\n25. Ejecutivo,\n26. El?ctrico/,\n27. Empaquetad,\n28. Empleado,\n29. Encargado,\n30. Enfermeria,\n31. Eventos,\n32. Fiambrer?a,\n33. Fonoaudiol,\n34. Funcionari,\n35. G?sfiter,\n36. Garz?n,\n37. Gerente,\n38. Guardia de,\n39. Hospital,\n40. Ingeniero,\n41. Inspector,\n42. Jardinero,\n43. Jefe,\n44. Jefe de lo,\n45. Jefe de ob,\n46. Jefe de se,\n47. Jornalero,\n48. Junior,\n49. Lavander?a,\n50. Maestro ce,\n51. Manicurist,\n52. Manipulado,\n53. Mantenimie,\n54. Mec?nico,\n55. Modista,\n56. Mueblista,\n57. Ninguno,\n58. No sabe/No,\n59. Nochero,\n60. Obrero,\n61. Oficina,\n62. Operario,\n63. Otros,\n64. Param?dico,\n65. Peluquera,\n66. Pesquero,\n67. Pintor/ ma,\n68. Prevencion,\n69. Profesiona,\n70. Profesor/e,\n71. Promotor,\n72. Psic?loga,\n73. Recepcioni,\n74. Repartidor,\n75. Reponedora,\n76. Secretaria,\n77. Servicio a,\n78. Supervisor,\n79. T?cnico,\n80. Vendedora",
"@type": "propertyValue"
},
{
"name": "a11_recod",
"description": " Podría decir si la remuneración de su trabajo principal, es: (Recodificado) (Adimark)",
"value": "1. 1,\n2. 2",
"@type": "propertyValue"
},
{
"name": "a48a49",
"description": "Personas con síntomas depresivos (Adimark)",
"value": "1. No,\n2. nr,\n3. S?",
"@type": "propertyValue"
},
{
"name": "depre",
"description": "Sintomatología depresiva",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "estrechez",
"description": "Estrechez Económica",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "mujer",
"description": "Sexo (1=Mujer)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "psicotrop",
"description": "Consumo de psicotrópicos",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "frec_acos21_1",
"description": "Autocategorización Acoso Frecuente (Varias veces por semana y A diario)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "frec_acos_ecotest",
"description": "Presencia acoso psicologico Araucaria (A22)",
"value": "1. 0,\n2. 1,\n3. 99",
"@type": "propertyValue"
},
{
"name": "testigo",
"description": "Testigo de acoso sexual (A28)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "k2_muyelevado",
"description": "Distrés muy elevado (>12 en suma de puntajes brutos)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "k2_elevado",
"description": "Distrés elevado o muy elevado (>6)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "k6_2",
"description": "Suma de puntajes brutos Distrés K6",
"@type": "propertyValue"
},
{
"name": "prec1",
"description": "Ausencia Contrato Escrito",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec2",
"description": "Contrato precario (Honorarios u otro)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec4",
"description": "Trabaja menos de 44 hrs y desea trabajar más",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec5",
"description": "Horario nocturno o por turnos",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec6",
"description": "Remuneración variable",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "antiguedad",
"description": "Antigüedad (Mayor a 6 meses) (A13)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "politrab",
"description": "Politrabajo (A14)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "desempleo",
"description": "Desempleo últimos 12 meses (A15)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "trabdom",
"description": "Trabajo Doméstico (A16)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "atpub",
"description": "Atención a Público",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "khab",
"description": "JCQ desarrollo de habilidades",
"@type": "propertyValue"
},
{
"name": "kcont",
"description": "JCQ control",
"@type": "propertyValue"
},
{
"name": "latdec",
"description": "JCQ latitud decisional",
"@type": "propertyValue"
},
{
"name": "latdec_dic",
"description": "Baja latitud decisional (Dicotomizada)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "democ",
"description": "Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21)",
"@type": "propertyValue"
},
{
"name": "prec8",
"description": "Desempleo últimos 12 meses",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec9",
"description": "Ingreso Personal Mensual (<350000 pesos)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "estabilidad",
"description": "Baja estabilidad laboral",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec7",
"description": "Baja estabilidad laboral",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "democ_dic",
"description": "Altas demandas emocionales (Dicotomizada)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul1",
"description": "Tiene miedo de reclamar mejores condiciones de trabajo",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul2",
"description": "Se siente indefenso/a ante el trato injusto de sus superiores",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul3",
"description": "Se siente preocupado de que le cambien las condiciones de su salario",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul4",
"description": "Tiene miedo de que lo despidan si no hace lo que le piden",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul5",
"description": "Se siente preocupado(a) de que lo/a despidan o no le renueven el contrato",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul6",
"description": "Se siente preocupado por lo difícil que sería encontrar otro trabajo en caso que lo despidan",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul7",
"description": "Le hacen sentir que usted puede ser fácilmente reemplazado (a)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "vul",
"description": "Vulnerabilidad, suma de puntajes (1= 3 o más)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "auditc",
"description": "Consumo riesgoso de Alcohol (ACC)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec3r",
"description": "Trabaja menos de 44 hrs y desea trabajar más",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "aposoc1",
"description": "Mi jefe inmediato presta atención a lo que yo digo (considera mi punto de vista) (recodificada sin NR)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "aposoc2",
"description": "Mi jefe inmediato consigue que la gente trabaje junta (recodificada sin NR)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "aposoc3",
"description": "Mi jefe inmediato facilita la realización del trabajo (recodificada sin NR)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "aposoc4",
"description": "Los(as) colegas con quienes trabajo se interesan o preocupan por mí (recodificada sin NR)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "aposoc5",
"description": "Mis colegas facilitan la realización de mi trabajo (recodificada sin NR)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "aposoc6",
"description": "En mi trabajo tengo la impresión de formar parte de un equipo (recodificada sin NR)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "aposoc_tot",
"description": "Bajo apoyo social",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "tenpsi",
"description": "JOBSTRAIN/Tensión Psíquica (Dicotomizada)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "iso",
"description": "ISOSTRAIN",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "salgen",
"description": "Salud General (1=Mala o Muy Mala)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "climasex",
"description": "Clima Sexista",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "culorg1",
"description": "CO orientada a Resultados (vs. a Procesos) (Art JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "culorg2",
"description": "CO orientada a Cumplimiento de Tareas (vs. Bienestar de Trabajadores) (Art JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "culorg3",
"description": "CO orientada a Control Estricto (vs. Laxo) (Art JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "cultura",
"description": "C.O. Orientada a resultados, tareas y rígida",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des1",
"description": "Variable LID_DES1 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des2",
"description": "Variable LID_DES2 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des3",
"description": "Variable LID_DES3 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des4",
"description": "Variable LID_DES4 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des5",
"description": "Variable LID_DES5 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des6",
"description": "Variable LID_DES6 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des7",
"description": "Variable LID_DES7 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des8",
"description": "Variable LID_DES8 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des9",
"description": "Variable LID_DES9 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des10",
"description": "Variable LID_DES10 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des11",
"description": "Variable LID_DES11 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des12",
"description": "Variable LID_DES12 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des13",
"description": "Variable LID_DES13 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des14",
"description": "Variable LID_DES14 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lid_des1_rec",
"description": "Variable LID_DES1 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des2_rec",
"description": "Variable LID_DES2 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des3_rec",
"description": "Variable LID_DES3 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des4_rec",
"description": "Variable LID_DES4 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des5_rec",
"description": "Variable LID_DES5 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des6_rec",
"description": "Variable LID_DES6 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des7_rec",
"description": "Variable LID_DES7 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des8_rec",
"description": "Variable LID_DES8 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des9_rec",
"description": "Variable LID_DES9 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des10_rec",
"description": "Variable LID_DES10 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des11_rec",
"description": "Variable LID_DES11 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des12_rec",
"description": "Variable LID_DES12 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des13_rec",
"description": "Variable LID_DES13 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des14_rec",
"description": "Variable LID_DES14 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lf",
"description": "Suma de puntajes brutos Liderazgo Laissez-Faire (todas las variables)",
"@type": "propertyValue"
},
{
"name": "la",
"description": "Suma de puntajes brutos Liderazgo Autoritario",
"@type": "propertyValue"
},
{
"name": "lc",
"description": "Suma de puntajes brutos Liderazgo Constructivo (todas las variables)",
"@type": "propertyValue"
},
{
"name": "naq1",
"description": "Variable NAQ1 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq2",
"description": "Variable NAQ2 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq3",
"description": "Variable NAQ3 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq4",
"description": "Variable NAQ4 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq5",
"description": "Variable NAQ5 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq6",
"description": "Variable NAQ6 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq7",
"description": "Variable NAQ7 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq8",
"description": "Variable NAQ8 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq9",
"description": "Variable NAQ9 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq10",
"description": "Variable NAQ10 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq11",
"description": "Variable NAQ11 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq12",
"description": "Variable NAQ12 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq13",
"description": "Variable NAQ13 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq14",
"description": "Variable NAQ14 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq15",
"description": "Variable NAQ15 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq16",
"description": "Variable NAQ16 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq17",
"description": "Variable NAQ17 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq18",
"description": "Variable NAQ18 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq19",
"description": "Variable NAQ19 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq20",
"description": "Variable NAQ20 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq21",
"description": "Variable NAQ21 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq22",
"description": "Variable NAQ22 recodificada (sin datos perdidos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq1_rec",
"description": "Variable NAQ1 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq2_rec",
"description": "Variable NAQ2 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq3_rec",
"description": "Variable NAQ3 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq4_rec",
"description": "Variable NAQ4 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq5_rec",
"description": "Variable NAQ5 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq6_rec",
"description": "Variable NAQ6 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq7_rec",
"description": "Variable NAQ7 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq8_rec",
"description": "Variable NAQ8 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq9_rec",
"description": "Variable NAQ9 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq10_rec",
"description": "Variable NAQ10 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq11_rec",
"description": "Variable NAQ11 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq12_rec",
"description": "Variable NAQ12 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq13_rec",
"description": "Variable NAQ13 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq14_rec",
"description": "Variable NAQ14 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq15_rec",
"description": "Variable NAQ15 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq16_rec",
"description": "Variable NAQ16 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq17_rec",
"description": "Variable NAQ17 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq18_rec",
"description": "Variable NAQ18 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq19_rec",
"description": "Variable NAQ19 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq20_rec",
"description": "Variable NAQ20 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq21_rec",
"description": "Variable NAQ21 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq22_rec",
"description": "Variable NAQ22 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_dic",
"description": "Suma puntajes dicotomizado (NAQ) (Dichotomized sum score)",
"@type": "propertyValue"
},
{
"name": "naq_leyman",
"description": "NAQ Criterio Leymann",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_my_e",
"description": "NAQ Criterio Mikkelsen y Einarsen",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_a",
"description": "NAQ Criterio Agervold",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "mas_horas",
"description": "Desearía trabajar más horas a la semana (A9)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "menos45",
"description": "Trabajo principal de menos 45 horas (A8)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "quieren_mas",
"description": "Desearía trabajar más horas a la semana (A9)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec3",
"description": "Trabaja menos de 45 horas semanales y desearia trabajar más",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prec",
"description": "Indicador de precariedad",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "precr",
"description": "Indicador de precariedad",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "precr1",
"description": "3 o más indicadores de precariedad laboral (de suma prec1 2 3 5 6 7 8 y vul)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "tamano",
"description": "Tamaño de la organización (Adimark)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "r1",
"description": "Mis superiores me dan el reconocimiento que merezco",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "r2",
"description": "Las oportunidades de promoción en mi trabajo son escasas",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "r3",
"description": "Me inquieta/angustia que se empeoren mis condiciones de trabajo (horario, carga laboral, salario, etc.)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "r4",
"description": "Mi estabilidad laboral es baja",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "r5",
"description": "Si pienso en todo el trabajo y esfuerzo que he realizado, el reconocimiento que recibo en mi trabajo me parece adecuado",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "r6",
"description": "Si pienso en todo el trabajo y esfuerzo que he realizado mis oportunidades de ascender me parecen adecuadas",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "r7",
"description": "Si pienso en todo el trabajo y esfuerzo que he realizado, mi sueldo me parece adecuado",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "e1",
"description": "A menudo debido a la cantidad de tareas que tengo trabajo a un ritmo muy apurado",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "e2",
"description": "Me interrumpen o molestan con frecuencia en mi trabajo",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "e3",
"description": "En el último tiempo tengo cada vez más trabajo",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "esfuerzo",
"description": "Suma Esfuerzos",
"@type": "propertyValue"
},
{
"name": "recompensas",
"description": "Suma Recompensas",
"@type": "propertyValue"
},
{
"name": "la_rec",
"description": "Suma ptjes dicotomizados Leyman (1,4,8,10) Lid Autoritario",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "lf_rec",
"description": "Suma ptjes dicotomizados Leyman (5,7,9) Lid Laissez-Faire",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "lc_rec",
"description": "Suma ptjes dicotomizados Leyman (6,11,12,14) Lid Constructivo",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "lf_leyman",
"description": "Exposición a Lid. Laissez-Faire (Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "la_leyman",
"description": "Exposición a Lid. Autoritario/Tiránico (Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lc_leyman",
"description": "Exposición a Lid. Constructivo (Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_sum",
"description": "Acosados de vez en cuando o víctimas de acoso (Puntaje Bruto NAQ)",
"@type": "propertyValue"
},
{
"name": "k6",
"description": "Distrés elevado o muy elevado (>6)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "s1",
"description": "Me siento bastante satisfecho con mi trabajo actual",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "s2",
"description": "Casi todos los días estoy entusiasmado con mi trabajo",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "s3",
"description": "Cada día en el trabajo parece que nunca terminará",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "s4",
"description": "Disfruto con mi trabajo",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "s5",
"description": "Considero que mi trabajo es bastante desagradable (invertido)",
"value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
"@type": "propertyValue"
},
{
"name": "satis",
"description": "Suma de Puntajes Brutos Satisfacción Laboral",
"@type": "propertyValue"
},
{
"name": "ocuprev",
"description": "Ocupación Araucaria (codificada erroneamente, ver ocuprev rec)",
"value": "1. Conductores transporte y Operadores m�quinas,\n2. Empleados/as de Oficina,\n3. Jefaturas Intermedias,\n4. Operarios/as,\n5. Profesionales,\n6. T�cnicos/as,\n7. Trabajadores de los servicios,\n8. Trabajadores no calificados,\n9. Vendedores",
"@type": "propertyValue"
},
{
"name": "naq_r_af_rec",
"description": "Suma de puntajes dic (Al menos una vez a la semana) Acoso Físico",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "naq_r_ap_rec",
"description": "Suma de puntajes dic (Al menos una vez a la semana) Acoso Psicológico",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4,\n6. 5,\n7. 6,\n8. 7,\n9. 8,\n10. 9,\n11. 10,\n12. 11,\n13. 12",
"@type": "propertyValue"
},
{
"name": "naq_r_rt_rec",
"description": "Suma de puntajes dic (Al menos una vez a la semana) Rel con Trabajo",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4,\n6. 5,\n7. 6,\n8. 7",
"@type": "propertyValue"
},
{
"name": "naq_af_leyman",
"description": "NAQ Acoso Físico (Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_ap_leyman",
"description": "NAQ Acoso Psicológico (Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_rt_leyman",
"description": "NAQ Relacionado con el Trabajo (Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_af_my_e",
"description": "NAQ Acoso Físico (Mikkelsen y Einarsen)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_ap_my_e",
"description": "NAQ Acoso Psicológico (Mikkelsen y Einarsen)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_rt_my_e",
"description": "NAQ Relacionado con el Trabajo (Mikkelsen y Einarsen)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_af_a",
"description": "NAQ Acoso Físico (Agervold)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_ap_a",
"description": "NAQ Acoso Psicológico (Agervold)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_rt_a",
"description": "NAQ Relacionado con el Trabajo (Agervold)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "satlab_rec",
"description": "Baja Satisfacción Laboral (1er cuartil suma de puntajes)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "araucaria_acoso",
"description": "Suma acoso sexual, psicológico y físico, Araucaria",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "acoso_auto",
"description": "Suma acoso sexual, psicológico y físico, Araucaria, más Autocategorización NAQ",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "acoso_auto_rec",
"description": "Al menos una forma de victimización acoso sexual, físico, psi o autonaq",
"value": "1. Expuesto,\n2. No expuest",
"@type": "propertyValue"
},
{
"name": "naq_sum_roc",
"description": "Variable Dicotomizada Suma Puntajes Brutos NAQ Curvas ROC (>11 ptos) (tesis ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_sum_roc_3cat",
"description": "Diferencia entre quienes obtienen >11 ptos, >9 ptos, y 0 a 8 (tesis ags)",
"value": "1. 0,\n2. 1,\n3. 2",
"@type": "propertyValue"
},
{
"name": "hombre",
"description": "Sexo Hombre (1=Hombre)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "autonaq",
"description": "Autocategorización según Definición NAQ (A21)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq23",
"description": "Variable NAQ23 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq24",
"description": "Variable NAQ24 recodificada (sin datos perdidos) no utilizada para cálculos indicadores NAQ",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq23_rec",
"description": "Variable NAQ23 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq24_rec",
"description": "Variable NAQ24 recodificada (sin datos perdidos en base a criterio Leymann)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "naq_r_incivismo_rec",
"description": "Escala Incivismo NAQ (Suma items 7, 14, 23 y 24) (Am y Xi 2018)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4",
"@type": "propertyValue"
},
{
"name": "naq_inciv_leyman",
"description": "Escala Incivismo NAQ (>0) (Am y Xi 2018)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "prev3",
"description": "Ocupación Araucaria (Operarios, Técnicos y No Calificados v/s Oficinistas, Servicios, Jefaturas y Profesionales) (Art JPT)",
"value": "1. 1,\n2. 2",
"@type": "propertyValue"
},
{
"name": "superv",
"description": "Supervisa a otros",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "ocuprev_rec",
"description": "Ocupación Araucaria (Recodificada 5= Vendedores) (Art JPT)",
"value": "1. Conductores transporte y Operadores m�quinas,\n2. Empleados/as de Oficina,\n3. Jefaturas Intermedias,\n4. Operarios/as,\n5. Profesionales,\n6. T�cnicos/as,\n7. Trabajadores de los servicios,\n8. Trabajadores no calificados,\n9. Vendedores",
"@type": "propertyValue"
},
{
"name": "sect_priv",
"description": "Trabaja Organismo Público o Empresa Privada (1=Sector privado)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "cont_trab",
"description": "Ausencia de Contrato Escrito (1= Sin Contrato)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "org_sind",
"description": "Existencia de Organización Sindical o Gremial (1=No)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "gse_ac3",
"description": "GSE Encuestado (D y E) (1= DyE)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "jef_hogar",
"description": "Jefe de Hogar (1=Sí)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "agr_fisica",
"description": "Agresión física últimos 12 meses",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "acos_sex",
"description": "Acoso sexual últimos 12 meses",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lf_rec_logit",
"description": "Suma puntajes dicotomizados Lid Laissez-Faire Leymann (OCM), 3 niveles(ags)",
"value": "1. 0,\n2. 1,\n3. 2",
"@type": "propertyValue"
},
{
"name": "la_rec_logit",
"description": "Suma puntajes dicotomizados Lid Autoritario Leymann (OCM), 3 niveles(ags)",
"value": "1. 0,\n2. 1,\n3. 2",
"@type": "propertyValue"
},
{
"name": "lc_rec_logit",
"description": "Suma puntajes dicotomizados Lid Constructivo Leymann (OCM), 3 niveles(ags)",
"value": "1. 0,\n2. 1,\n3. 2",
"@type": "propertyValue"
},
{
"name": "naq_dic_logit",
"description": "Suma puntajes dicotomizados NAQ Leymann (OCM), 3 niveles(ags)",
"value": "1. 0,\n2. 1,\n3. 2",
"@type": "propertyValue"
},
{
"name": "lid_des_sum",
"description": "Suma puntajes brutos (5, 7, 9, 1, 4, 8, 10) Lid Destructivo",
"@type": "propertyValue"
},
{
"name": "lid_des_sum_cuartil",
"description": "NAQ por cuartiles (Por defecto, con 2000 casos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "naq_sum_cuartil",
"description": "Suma ptjes brutos NAQ-R (por cuartiles) (correcto 1995 casos)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_1",
"description": "Pertenencia Esperada a la Clase 1 (>.5), Cultura por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_2",
"description": "Pertenencia Esperada a la Clase 2 (>.5), Cultura por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_3",
"description": "Pertenencia Esperada a la Clase 3 (>.5), Cultura por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_4",
"description": "Pertenencia Esperada a la Clase 4 (>.5), Cultura por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_5",
"description": "Pertenencia Esperada a la Clase 5 (>.5), Cultura por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_6",
"description": "Pertenencia Esperada a la Clase 6 (>.5), Cultura por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "cluster_rec",
"description": "Conjunto concatenado de clases (JPT)",
"@type": "propertyValue"
},
{
"name": "cluster_rec2",
"description": "Recodificación Clases Latentes Cultura (1=.;2=6;3=5;4=4;5=3;6=2;7=1) (JPT)",
"value": "1. clus 1,\n2. clus 2,\n3. clus 3,\n4. clus 4,\n5. clus 5,\n6. clus 6",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_alt_1",
"description": "Pertenencia Esperada a la Clase 1 (>.5), Cultura Alternativa por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_alt_2",
"description": "Pertenencia Esperada a la Clase 2 (>.5), Cultura Alternativa por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_alt_3",
"description": "Pertenencia Esperada a la Clase 3 (>.5), Cultura Alternativa por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_alt_4",
"description": "Pertenencia Esperada a la Clase 4 (>.5), Cultura Alternativa por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_alt_5",
"description": "Pertenencia Esperada a la Clase 5 (>.5), Cultura Alternativa por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_culorg_alt_6",
"description": "Pertenencia Esperada a la Clase 6 (>.5), Cultura Alternativa por Clases (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "classprior1",
"description": "Probabilidad A priori de Pertenecer a la Clase 1, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classprior2",
"description": "Probabilidad A priori de Pertenecer a la Clase 2, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classprior3",
"description": "Probabilidad A priori de Pertenecer a la Clase 3, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classprior4",
"description": "Probabilidad A priori de Pertenecer a la Clase 4, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classprior5",
"description": "Probabilidad A priori de Pertenecer a la Clase 5, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classprior6",
"description": "Probabilidad A priori de Pertenecer a la Clase 6, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_alt_1",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 1 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_alt_2",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 2 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_alt_3",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 3 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_alt_4",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 4 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_alt_5",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 5 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_alt_6",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura Alternativa, clase 6 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "cluster_alt",
"description": "Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT)",
"@type": "propertyValue"
},
{
"name": "cluster_rec_alt",
"description": "Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) (JPT)",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_1",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 1 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_2",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 2 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_3",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 3 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_4",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 4 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_5",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 5 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_culorg_6",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de Cultura, clase 6 (6 clases) (JPT)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "naq_r_af_sum",
"description": "Suma de puntajes brutos NAQ Acoso Físico (ags)",
"@type": "propertyValue"
},
{
"name": "naq_r_ap_sum",
"description": "Suma de puntajes brutos NAQ Acoso Psicológico (ags)",
"@type": "propertyValue"
},
{
"name": "naq_r_rt_sum",
"description": "Suma de puntajes brutos NAQ Acoso Relacionado con el Trabajo (ags)",
"@type": "propertyValue"
},
{
"name": "democ_paper",
"description": "Demandas Psicológicas (JCQ) y Demandas Emocionales (ISTAS 21) (ags)",
"@type": "propertyValue"
},
{
"name": "lf2",
"description": "Suma de puntajes brutos Liderazgo Laissez-Faire (solo variables validadas) (ags)",
"@type": "propertyValue"
},
{
"name": "lc2",
"description": "Suma de puntajes brutos Liderazgo Constructivo (solo variables validadas) (ags)",
"@type": "propertyValue"
},
{
"name": "lid_des_dic",
"description": "Suma puntajes dicotomizado (DLS) (Dichotomized sum score) (ags)",
"value": "1. 0,\n2. 1,\n3. 2,\n4. 3,\n5. 4 o más",
"@type": "propertyValue"
},
{
"name": "lid_des_dic_logit",
"description": "Suma puntajes dicotomizados DLS Leymann (OCM), 3 niveles(ags)",
"value": "1. 0,\n2. 1,\n3. 2",
"@type": "propertyValue"
},
{
"name": "bajarecompensa",
"description": "Baja Recompensa (Art JPT)",
"value": "1. Alta recompensa,\n2. Baj recompensa",
"@type": "propertyValue"
},
{
"name": "altoesfuerzo",
"description": "Alto Esfuerzo (Art JPT)",
"value": "1. Alto esfuerzo,\n2. Baj esfuerzo",
"@type": "propertyValue"
},
{
"name": "desbalance",
"description": "esfuerzo/(recompensas* 0.4285)",
"@type": "propertyValue"
},
{
"name": "desbalance_dic",
"description": "Desbalance Esfuerzo-Recompensa",
"value": "1. Balance Esf-Rec,\n2. DER",
"@type": "propertyValue"
},
{
"name": "culorg1_ext",
"description": "CO Desbalanceada en Eje Resultados vs. Procesos (puntajes 3 y 4=0)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "culorg2_ext",
"description": "CO Desbalanceada en Eje Control Estricto vs. Laxo (puntajes 3 y 4=0)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "culorg3_ext",
"description": "CO Desbalanceada en Eje Tareas vs. Bienestar Trabajadores (puntajes 3 y 4=0)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "proc_res",
"description": "CO Balanceada en Eje Resultados vs. Procesos (1=3/4) (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "bienst_tarea",
"description": "CO Balanceada en Eje Tareas vs. Bienestar Trabajadores (1=3/4) (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "laxo_estr",
"description": "CO Balanceada en Eje Control Estricto vs. Laxo (1=3/4) (JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "lid_des_my_e",
"description": "Exposición a Liderazgos Destructivos (OCM Mikkelsen y Einarsen) (Art JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "cluster_rec2_binary",
"description": "Clase a Comparar con el Resto de las Clases",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "classpost_culorg_alt1",
"description": "Probabilidad Posterior de Pertencer a la Clase 1, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classpost_culorg_alt2",
"description": "Probabilidad Posterior de Pertencer a la Clase 2, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classpost_culorg_alt3",
"description": "Probabilidad Posterior de Pertencer a la Clase 3, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classpost_culorg_alt4",
"description": "Probabilidad Posterior de Pertencer a la Clase 4, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classpost_culorg_alt5",
"description": "Probabilidad Posterior de Pertencer a la Clase 5, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "classpost_culorg_alt6",
"description": "Probabilidad Posterior de Pertencer a la Clase 6, CULTURA Definitiva por Clases (JPT)",
"@type": "propertyValue"
},
{
"name": "cluster_rec2_alt",
"description": "Clasificación por Cluster, Ítemes de Cultura (6 clases)",
"value": "1. clus 1,\n2. clus 2,\n3. clus 3,\n4. clus 4,\n5. clus 5,\n6. clus 6",
"@type": "propertyValue"
},
{
"name": "naq_sum_roc3",
"description": "Suma ptjes brutos NAQ-R ROC (crit. inclusivo) (Art Xi y Am) (tesis ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "educacion277",
"value": "1. Sin Educacion o Basica,\n2. Media o Tecnica Incomp,\n3. Tecnica Comp o Univ Incomp,\n4. Universitaria Comp o Postgr",
"@type": "propertyValue"
},
{
"name": "ocuprev_xi_am277",
"value": "1. Jef Intermedias,\n2. Prof y Tecnicos,\n3. Emp Oficina y Vendedores,\n4. Op, conductores y calif,\n5. No calificados",
"@type": "propertyValue"
},
{
"name": "ing_hog_xi_am277",
"value": "1. Hasta 540.000 pesos,\n2. Desde 540.001 hasta 913.000 pesos,\n3. Desde 913.001 o mas",
"@type": "propertyValue"
},
{
"name": "vul_hm",
"value": "1. Vulnerable Mujer,\n2. Vulnerable Hombre",
"@type": "propertyValue"
},
{
"name": "lf_leyman_hm",
"value": "1. Exp a Lid LF Mujer,\n2. Exp a Lid LF Hombre",
"@type": "propertyValue"
},
{
"name": "la_leyman_hm",
"value": "1. Exp a Lid Aut Mujer,\n2. Exp a Lid Aut Hombre",
"@type": "propertyValue"
},
{
"name": "lc_leyman_hm",
"value": "1. Exp a Lid Cons Mujer,\n2. Exp a Lid Cons Hombre",
"@type": "propertyValue"
},
{
"name": "depre_hm",
"value": "1. Exp a SintDep Mujer,\n2. Exp a SintDep Hombre",
"@type": "propertyValue"
},
{
"name": "psicotrop_hm",
"value": "1. Exp a ConsPsic Mujer,\n2. Exp a ConsPsic Hombre",
"@type": "propertyValue"
},
{
"name": "k2_elevado_hm",
"value": "1. Exp a Distres Mujer,\n2. Exp a Distres Hombre",
"@type": "propertyValue"
},
{
"name": "climasex_hm",
"value": "1. Exp a ClimaSex Mujer,\n2. Exp a ClimaSex Hombre",
"@type": "propertyValue"
},
{
"name": "jerarquia_acoso",
"value": "1. Superiores,\n2. Companeros,\n3. Subalternos,\n4. Externos",
"@type": "propertyValue"
},
{
"name": "jerarquia_acoso2",
"value": "1. Companeros,\n2. Subalternos,\n3. Externos",
"@type": "propertyValue"
},
{
"name": "lid_des_leyman",
"description": "Exposición a Liderazgos Destructivos (OCM Leyman) (Art JPT)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "acos_psico_freq",
"description": "Acoso Psicológico Seguido y Muy Seguido (Art. Ximena y Amalia)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "carga_dom",
"description": "Y sin considerar su trabajo remunerado, durante la semana, ¿usted dedica además tiempo para hacer tareas domésticas y/o de cuidado, pero no remunerado? (A16)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "educacion_media",
"description": "Educación Menor a Nivel de Enseñanza Media Completa (Art. Magdalena)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "cont_def",
"description": "Contrato Definido (Art. Magdalena)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "ocuprev_nocalif",
"description": "Ocupación Sin Calificación (Operarios, Técnicos, Conductores y Trabajdores No-Calificados) (Art. Magdalena)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "santiago",
"description": "Zona, Gran Santiago",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "valpo",
"description": "Zona, Gran Valparaiso",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "concep",
"description": "Zona, Gran Concepción",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "classpost_naq_def_1",
"description": "Probabilidad Posterior de Pertencer a la Clase 1, NAQ por Clases (ags)",
"@type": "propertyValue"
},
{
"name": "classpost_naq_def_2",
"description": "Probabilidad Posterior de Pertencer a la Clase 2, NAQ por Clases (ags)",
"@type": "propertyValue"
},
{
"name": "classpost_naq_def_3",
"description": "Probabilidad Posterior de Pertencer a la Clase 3, NAQ por Clases (ags)",
"@type": "propertyValue"
},
{
"name": "classpost_naq_def_4",
"description": "Probabilidad Posterior de Pertencer a la Clase 4, NAQ por Clases (ags)",
"@type": "propertyValue"
},
{
"name": "classpost_naq_def_5",
"description": "Probabilidad Posterior de Pertencer a la Clase 5, NAQ por Clases (ags)",
"@type": "propertyValue"
},
{
"name": "classpost_naq_def_6",
"description": "Probabilidad Posterior de Pertencer a la Clase 6, NAQ por Clases (ags)",
"@type": "propertyValue"
},
{
"name": "expclass_naq_alt_1",
"description": "Pertenencia Esperada a la Clase 1 (>.6), NAQ por Clases (ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_naq_alt_2",
"description": "Pertenencia Esperada a la Clase 2 (>.6), NAQ por Clases (ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_naq_alt_3",
"description": "Pertenencia Esperada a la Clase 3 (>.6), NAQ por Clases (ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_naq_alt_4",
"description": "Pertenencia Esperada a la Clase 4 (>.6), NAQ por Clases (ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_naq_alt_5",
"description": "Pertenencia Esperada a la Clase 5 (>.6), NAQ por Clases (ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "expclass_naq_alt_6",
"description": "Pertenencia Esperada a la Clase 6 (>.6), NAQ por Clases (ags)",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_naq_alt_1",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 1 (6 clases) (ags)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_naq_alt_2",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 2 (6 clases) (ags)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_naq_alt_3",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 3 (6 clases) (ags)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_naq_alt_4",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 4 (6 clases) (ags)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_naq_alt_5",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 5 (6 clases) (ags)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "rec_expclass_naq_alt_6",
"description": "Clase Esperada (sin 0), Clasificación por Cluster, Ítemes de NAQ Definitiva, clase 6 (6 clases) (ags)",
"value": "1. 1",
"@type": "propertyValue"
},
{
"name": "cluster_naq_alt",
"description": "Conjunto concatenado de clases definitivo (contiene a los que no pertenecen) NAQ (ags)",
"@type": "propertyValue"
},
{
"name": "cluster_naq_alt_rec",
"description": "Clasificación preliminar (contiene a los que no pertenecen) NAQ (ags)",
"@type": "propertyValue"
},
{
"name": "cluster_rec2_naq_alt",
"description": "Clasificación por Cluster, Ítemes de NAQ (6 clases)",
"value": "1. class 1,\n2. class 2,\n3. class 3,\n4. class 4,\n5. class 5,\n6. class 6",
"@type": "propertyValue"
},
{
"name": "turnos",
"description": "Sistema de turnos",
"value": "1. 0,\n2. 1",
"@type": "propertyValue"
}
]
}`